{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Hackton organized by American Association of Petroleum geologists (AAPG) in Houston on July 2018\n",
    "#Given a dataset of oil,gas, and water production (volve_oil) containing both monthly and daily productions.\n",
    "#Final Goal is to apply LSTM on this dataset and predict the production \n",
    "##given the parameters measured during production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEPRD</th>\n",
       "      <th>WELL_BORE_CODE</th>\n",
       "      <th>NPD_WELL_BORE_CODE</th>\n",
       "      <th>NPD_WELL_BORE_NAME</th>\n",
       "      <th>NPD_FIELD_CODE</th>\n",
       "      <th>NPD_FIELD_NAME</th>\n",
       "      <th>NPD_FACILITY_CODE</th>\n",
       "      <th>NPD_FACILITY_NAME</th>\n",
       "      <th>ON_STREAM_HRS</th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>...</th>\n",
       "      <th>AVG_CHOKE_UOM</th>\n",
       "      <th>AVG_WHP_P</th>\n",
       "      <th>AVG_WHT_P</th>\n",
       "      <th>DP_CHOKE_SIZE</th>\n",
       "      <th>BORE_OIL_VOL</th>\n",
       "      <th>BORE_GAS_VOL</th>\n",
       "      <th>BORE_WAT_VOL</th>\n",
       "      <th>BORE_WI_VOL</th>\n",
       "      <th>FLOW_KIND</th>\n",
       "      <th>WELL_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-04-07</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-08</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-09</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-10</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.37614</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>33.09788</td>\n",
       "      <td>10.47992</td>\n",
       "      <td>33.07195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DATEPRD WELL_BORE_CODE  NPD_WELL_BORE_CODE NPD_WELL_BORE_NAME  \\\n",
       "0 2014-04-07  NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
       "1 2014-04-08  NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
       "2 2014-04-09  NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
       "3 2014-04-10  NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
       "4 2014-04-11  NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
       "\n",
       "   NPD_FIELD_CODE NPD_FIELD_NAME  NPD_FACILITY_CODE NPD_FACILITY_NAME  \\\n",
       "0         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
       "1         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
       "2         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
       "3         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
       "4         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
       "\n",
       "   ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE    ...      AVG_CHOKE_UOM  AVG_WHP_P  \\\n",
       "0            0.0                0.00000    ...                  %    0.00000   \n",
       "1            0.0                    NaN    ...                  %    0.00000   \n",
       "2            0.0                    NaN    ...                  %    0.00000   \n",
       "3            0.0                    NaN    ...                  %    0.00000   \n",
       "4            0.0              310.37614    ...                  %   33.09788   \n",
       "\n",
       "   AVG_WHT_P  DP_CHOKE_SIZE BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL  \\\n",
       "0    0.00000        0.00000          0.0           0.0           0.0   \n",
       "1    0.00000        0.00000          0.0           0.0           0.0   \n",
       "2    0.00000        0.00000          0.0           0.0           0.0   \n",
       "3    0.00000        0.00000          0.0           0.0           0.0   \n",
       "4   10.47992       33.07195          0.0           0.0           0.0   \n",
       "\n",
       "   BORE_WI_VOL   FLOW_KIND  WELL_TYPE  \n",
       "0          NaN  production         WI  \n",
       "1          NaN  production         OP  \n",
       "2          NaN  production         OP  \n",
       "3          NaN  production         OP  \n",
       "4          NaN  production         OP  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Day = pd.read_excel('Volve_production_data.xlsx',sheet_name = 'Daily Production Data')\n",
    "df_Day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wellbore name</th>\n",
       "      <th>NPDCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>On Stream</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "      <th>Water</th>\n",
       "      <th>GI</th>\n",
       "      <th>WI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hrs</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>227.5</td>\n",
       "      <td>11142.5</td>\n",
       "      <td>1.59794e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>733.833</td>\n",
       "      <td>24902</td>\n",
       "      <td>3.49623e+06</td>\n",
       "      <td>783.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>705.917</td>\n",
       "      <td>19617.8</td>\n",
       "      <td>2.88666e+06</td>\n",
       "      <td>2068.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>742.417</td>\n",
       "      <td>15085.7</td>\n",
       "      <td>2.24937e+06</td>\n",
       "      <td>6243.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wellbore name  NPDCode    Year  Month On Stream      Oil          Gas  \\\n",
       "0           NaN      NaN     NaN    NaN       hrs      Sm3          Sm3   \n",
       "1    15/9-F-1 C   7405.0  2014.0    4.0     227.5  11142.5  1.59794e+06   \n",
       "2    15/9-F-1 C   7405.0  2014.0    5.0   733.833    24902  3.49623e+06   \n",
       "3    15/9-F-1 C   7405.0  2014.0    6.0   705.917  19617.8  2.88666e+06   \n",
       "4    15/9-F-1 C   7405.0  2014.0    7.0   742.417  15085.7  2.24937e+06   \n",
       "\n",
       "     Water   GI   WI  \n",
       "0      Sm3  Sm3  Sm3  \n",
       "1        0  NaN  NaN  \n",
       "2   783.48  NaN  NaN  \n",
       "3  2068.48  NaN  NaN  \n",
       "4  6243.98  NaN  NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the dataset- monthly\n",
    "df_Month = pd.read_excel('Volve_production_data.xlsx',sheet_name = 'Monthly Production Data')\n",
    "df_Month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15634, 24)\n"
     ]
    }
   ],
   "source": [
    "print(df_Day.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(527, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df_Month.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['DATEPRD', 'WELL_BORE_CODE', 'NPD_WELL_BORE_CODE', 'NPD_WELL_BORE_NAME',\n",
      "       'NPD_FIELD_CODE', 'NPD_FIELD_NAME', 'NPD_FACILITY_CODE',\n",
      "       'NPD_FACILITY_NAME', 'ON_STREAM_HRS', 'AVG_DOWNHOLE_PRESSURE',\n",
      "       'AVG_DOWNHOLE_TEMPERATURE', 'AVG_DP_TUBING', 'AVG_ANNULUS_PRESS',\n",
      "       'AVG_CHOKE_SIZE_P', 'AVG_CHOKE_UOM', 'AVG_WHP_P', 'AVG_WHT_P',\n",
      "       'DP_CHOKE_SIZE', 'BORE_OIL_VOL', 'BORE_GAS_VOL', 'BORE_WAT_VOL',\n",
      "       'BORE_WI_VOL', 'FLOW_KIND', 'WELL_TYPE'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_Day.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATEPRD                        0\n",
       "WELL_BORE_CODE                 0\n",
       "NPD_WELL_BORE_CODE             0\n",
       "NPD_WELL_BORE_NAME             0\n",
       "NPD_FIELD_CODE                 0\n",
       "NPD_FIELD_NAME                 0\n",
       "NPD_FACILITY_CODE              0\n",
       "NPD_FACILITY_NAME              0\n",
       "ON_STREAM_HRS                285\n",
       "AVG_DOWNHOLE_PRESSURE       6654\n",
       "AVG_DOWNHOLE_TEMPERATURE    6654\n",
       "AVG_DP_TUBING               6654\n",
       "AVG_ANNULUS_PRESS           7744\n",
       "AVG_CHOKE_SIZE_P            6715\n",
       "AVG_CHOKE_UOM               6473\n",
       "AVG_WHP_P                   6479\n",
       "AVG_WHT_P                   6488\n",
       "DP_CHOKE_SIZE                294\n",
       "BORE_OIL_VOL                6473\n",
       "BORE_GAS_VOL                6473\n",
       "BORE_WAT_VOL                6473\n",
       "BORE_WI_VOL                 9928\n",
       "FLOW_KIND                      0\n",
       "WELL_TYPE                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Day.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEPRD</th>\n",
       "      <th>WELL_BORE_CODE</th>\n",
       "      <th>NPD_WELL_BORE_NAME</th>\n",
       "      <th>NPD_FIELD_NAME</th>\n",
       "      <th>NPD_FACILITY_NAME</th>\n",
       "      <th>ON_STREAM_HRS</th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
       "      <th>AVG_DP_TUBING</th>\n",
       "      <th>AVG_ANNULUS_PRESS</th>\n",
       "      <th>AVG_CHOKE_SIZE_P</th>\n",
       "      <th>AVG_CHOKE_UOM</th>\n",
       "      <th>AVG_WHP_P</th>\n",
       "      <th>AVG_WHT_P</th>\n",
       "      <th>DP_CHOKE_SIZE</th>\n",
       "      <th>BORE_OIL_VOL</th>\n",
       "      <th>BORE_GAS_VOL</th>\n",
       "      <th>BORE_WAT_VOL</th>\n",
       "      <th>FLOW_KIND</th>\n",
       "      <th>WELL_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-04-07</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-08</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.003059</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-09</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979008</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-10</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545759</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.37614</td>\n",
       "      <td>96.87589</td>\n",
       "      <td>277.27826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.215987</td>\n",
       "      <td>%</td>\n",
       "      <td>33.09788</td>\n",
       "      <td>10.47992</td>\n",
       "      <td>33.07195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DATEPRD WELL_BORE_CODE NPD_WELL_BORE_NAME NPD_FIELD_NAME  \\\n",
       "0 2014-04-07  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "1 2014-04-08  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "2 2014-04-09  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "3 2014-04-10  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "4 2014-04-11  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "\n",
       "  NPD_FACILITY_NAME  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
       "0    MÆRSK INSPIRER            0.0                0.00000   \n",
       "1    MÆRSK INSPIRER            0.0                    NaN   \n",
       "2    MÆRSK INSPIRER            0.0                    NaN   \n",
       "3    MÆRSK INSPIRER            0.0                    NaN   \n",
       "4    MÆRSK INSPIRER            0.0              310.37614   \n",
       "\n",
       "   AVG_DOWNHOLE_TEMPERATURE  AVG_DP_TUBING  AVG_ANNULUS_PRESS  \\\n",
       "0                   0.00000        0.00000                0.0   \n",
       "1                       NaN            NaN                0.0   \n",
       "2                       NaN            NaN                0.0   \n",
       "3                       NaN            NaN                0.0   \n",
       "4                  96.87589      277.27826                0.0   \n",
       "\n",
       "   AVG_CHOKE_SIZE_P AVG_CHOKE_UOM  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE  \\\n",
       "0          0.000000             %    0.00000    0.00000        0.00000   \n",
       "1          1.003059             %    0.00000    0.00000        0.00000   \n",
       "2          0.979008             %    0.00000    0.00000        0.00000   \n",
       "3          0.545759             %    0.00000    0.00000        0.00000   \n",
       "4          1.215987             %   33.09788   10.47992       33.07195   \n",
       "\n",
       "   BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL   FLOW_KIND WELL_TYPE  \n",
       "0           0.0           0.0           0.0  production        WI  \n",
       "1           0.0           0.0           0.0  production        OP  \n",
       "2           0.0           0.0           0.0  production        OP  \n",
       "3           0.0           0.0           0.0  production        OP  \n",
       "4           0.0           0.0           0.0  production        OP  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop data columns that are  not necessary\n",
    "dff = df_Day.drop(['BORE_WI_VOL','NPD_WELL_BORE_CODE','NPD_FIELD_CODE','NPD_FACILITY_CODE'], axis = 1, inplace = False)\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Drop nan values in dff\n",
    "dff.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7504, 20)\n"
     ]
    }
   ],
   "source": [
    "print(dff.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEPRD</th>\n",
       "      <th>WELL_BORE_CODE</th>\n",
       "      <th>NPD_WELL_BORE_NAME</th>\n",
       "      <th>NPD_FIELD_NAME</th>\n",
       "      <th>NPD_FACILITY_NAME</th>\n",
       "      <th>ON_STREAM_HRS</th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
       "      <th>AVG_DP_TUBING</th>\n",
       "      <th>AVG_ANNULUS_PRESS</th>\n",
       "      <th>AVG_CHOKE_SIZE_P</th>\n",
       "      <th>AVG_CHOKE_UOM</th>\n",
       "      <th>AVG_WHP_P</th>\n",
       "      <th>AVG_WHT_P</th>\n",
       "      <th>DP_CHOKE_SIZE</th>\n",
       "      <th>BORE_OIL_VOL</th>\n",
       "      <th>BORE_GAS_VOL</th>\n",
       "      <th>BORE_WAT_VOL</th>\n",
       "      <th>FLOW_KIND</th>\n",
       "      <th>WELL_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-04-07</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.37614</td>\n",
       "      <td>96.87589</td>\n",
       "      <td>277.27826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.215987</td>\n",
       "      <td>%</td>\n",
       "      <td>33.09788</td>\n",
       "      <td>10.47992</td>\n",
       "      <td>33.07195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>303.50078</td>\n",
       "      <td>96.92339</td>\n",
       "      <td>281.44744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.087015</td>\n",
       "      <td>%</td>\n",
       "      <td>22.05334</td>\n",
       "      <td>8.70429</td>\n",
       "      <td>22.05334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-04-13</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>303.53481</td>\n",
       "      <td>96.95885</td>\n",
       "      <td>276.03200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.962365</td>\n",
       "      <td>%</td>\n",
       "      <td>27.50281</td>\n",
       "      <td>9.42315</td>\n",
       "      <td>16.16326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-04-14</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>303.78228</td>\n",
       "      <td>96.96873</td>\n",
       "      <td>282.78676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>%</td>\n",
       "      <td>20.99552</td>\n",
       "      <td>8.13137</td>\n",
       "      <td>20.73712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DATEPRD WELL_BORE_CODE NPD_WELL_BORE_NAME NPD_FIELD_NAME  \\\n",
       "0 2014-04-07  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "4 2014-04-11  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "5 2014-04-12  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "6 2014-04-13  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "7 2014-04-14  NO 15/9-F-1 C         15/9-F-1 C          VOLVE   \n",
       "\n",
       "  NPD_FACILITY_NAME  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
       "0    MÆRSK INSPIRER            0.0                0.00000   \n",
       "4    MÆRSK INSPIRER            0.0              310.37614   \n",
       "5    MÆRSK INSPIRER            0.0              303.50078   \n",
       "6    MÆRSK INSPIRER            0.0              303.53481   \n",
       "7    MÆRSK INSPIRER            0.0              303.78228   \n",
       "\n",
       "   AVG_DOWNHOLE_TEMPERATURE  AVG_DP_TUBING  AVG_ANNULUS_PRESS  \\\n",
       "0                   0.00000        0.00000                0.0   \n",
       "4                  96.87589      277.27826                0.0   \n",
       "5                  96.92339      281.44744                0.0   \n",
       "6                  96.95885      276.03200                0.0   \n",
       "7                  96.96873      282.78676                0.0   \n",
       "\n",
       "   AVG_CHOKE_SIZE_P AVG_CHOKE_UOM  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE  \\\n",
       "0          0.000000             %    0.00000    0.00000        0.00000   \n",
       "4          1.215987             %   33.09788   10.47992       33.07195   \n",
       "5          3.087015             %   22.05334    8.70429       22.05334   \n",
       "6          1.962365             %   27.50281    9.42315       16.16326   \n",
       "7          0.000000             %   20.99552    8.13137       20.73712   \n",
       "\n",
       "   BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL   FLOW_KIND WELL_TYPE  \n",
       "0           0.0           0.0           0.0  production        WI  \n",
       "4           0.0           0.0           0.0  production        OP  \n",
       "5           0.0           0.0           0.0  production        OP  \n",
       "6           0.0           0.0           0.0  production        OP  \n",
       "7           0.0           0.0           0.0  production        OP  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(dff['ON_STREAM_HRS'])\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dff.info()  #dff.tail()\n",
    "## TAKE A LOOK AT ONE WELL\n",
    "#select rows with a given code\n",
    "dff1 = dff.loc[dff['WELL_BORE_CODE'] == 'NO 15/9-F-1 C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dff1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>AVG_DP_TUBING</th>\n",
       "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
       "      <th>DP_CHOKE_SIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>310.376140</td>\n",
       "      <td>277.278260</td>\n",
       "      <td>96.875890</td>\n",
       "      <td>33.071950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>303.500780</td>\n",
       "      <td>281.447440</td>\n",
       "      <td>96.923390</td>\n",
       "      <td>22.053340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>303.534810</td>\n",
       "      <td>276.032000</td>\n",
       "      <td>96.958850</td>\n",
       "      <td>16.163260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>303.782280</td>\n",
       "      <td>282.786760</td>\n",
       "      <td>96.968730</td>\n",
       "      <td>20.737120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>303.858210</td>\n",
       "      <td>289.940670</td>\n",
       "      <td>97.021360</td>\n",
       "      <td>12.181530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>303.791870</td>\n",
       "      <td>299.671930</td>\n",
       "      <td>97.065690</td>\n",
       "      <td>1.490200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>304.335180</td>\n",
       "      <td>282.901000</td>\n",
       "      <td>96.919460</td>\n",
       "      <td>18.794840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>304.848590</td>\n",
       "      <td>273.700730</td>\n",
       "      <td>96.720340</td>\n",
       "      <td>28.502580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>305.371490</td>\n",
       "      <td>259.619930</td>\n",
       "      <td>96.615630</td>\n",
       "      <td>43.157100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>313.870580</td>\n",
       "      <td>282.814360</td>\n",
       "      <td>96.559530</td>\n",
       "      <td>28.484020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>301.375641</td>\n",
       "      <td>204.795183</td>\n",
       "      <td>102.676379</td>\n",
       "      <td>69.775570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>289.421362</td>\n",
       "      <td>182.059312</td>\n",
       "      <td>106.353209</td>\n",
       "      <td>78.935409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AVG_DOWNHOLE_PRESSURE  AVG_DP_TUBING  AVG_DOWNHOLE_TEMPERATURE  \\\n",
       "0                 0.000000       0.000000                  0.000000   \n",
       "4               310.376140     277.278260                 96.875890   \n",
       "5               303.500780     281.447440                 96.923390   \n",
       "6               303.534810     276.032000                 96.958850   \n",
       "7               303.782280     282.786760                 96.968730   \n",
       "8               303.858210     289.940670                 97.021360   \n",
       "9               303.791870     299.671930                 97.065690   \n",
       "10              304.335180     282.901000                 96.919460   \n",
       "11              304.848590     273.700730                 96.720340   \n",
       "12              305.371490     259.619930                 96.615630   \n",
       "13              313.870580     282.814360                 96.559530   \n",
       "14              301.375641     204.795183                102.676379   \n",
       "15              289.421362     182.059312                106.353209   \n",
       "745               0.000000       0.000000                  0.000000   \n",
       "\n",
       "     DP_CHOKE_SIZE  \n",
       "0         0.000000  \n",
       "4        33.071950  \n",
       "5        22.053340  \n",
       "6        16.163260  \n",
       "7        20.737120  \n",
       "8        12.181530  \n",
       "9         1.490200  \n",
       "10       18.794840  \n",
       "11       28.502580  \n",
       "12       43.157100  \n",
       "13       28.484020  \n",
       "14       69.775570  \n",
       "15       78.935409  \n",
       "745       0.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select certain columns from dff1\n",
    "#Extract only the needed columns from the well\n",
    "#dff1.corr()\n",
    "dff1P = dff1.loc[:,['AVG_DOWNHOLE_PRESSURE','AVG_DP_TUBING','AVG_DOWNHOLE_TEMPERATURE','DP_CHOKE_SIZE']]\n",
    "dff1P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>AVG_DP_TUBING</th>\n",
       "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
       "      <th>DP_CHOKE_SIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>310.376140</td>\n",
       "      <td>277.278260</td>\n",
       "      <td>96.875890</td>\n",
       "      <td>33.071950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>303.500780</td>\n",
       "      <td>281.447440</td>\n",
       "      <td>96.923390</td>\n",
       "      <td>22.053340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>303.534810</td>\n",
       "      <td>276.032000</td>\n",
       "      <td>96.958850</td>\n",
       "      <td>16.163260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>303.782280</td>\n",
       "      <td>282.786760</td>\n",
       "      <td>96.968730</td>\n",
       "      <td>20.737120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>303.858210</td>\n",
       "      <td>289.940670</td>\n",
       "      <td>97.021360</td>\n",
       "      <td>12.181530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>303.791870</td>\n",
       "      <td>299.671930</td>\n",
       "      <td>97.065690</td>\n",
       "      <td>1.490200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>304.335180</td>\n",
       "      <td>282.901000</td>\n",
       "      <td>96.919460</td>\n",
       "      <td>18.794840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>304.848590</td>\n",
       "      <td>273.700730</td>\n",
       "      <td>96.720340</td>\n",
       "      <td>28.502580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>305.371490</td>\n",
       "      <td>259.619930</td>\n",
       "      <td>96.615630</td>\n",
       "      <td>43.157100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>313.870580</td>\n",
       "      <td>282.814360</td>\n",
       "      <td>96.559530</td>\n",
       "      <td>28.484020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>301.375641</td>\n",
       "      <td>204.795183</td>\n",
       "      <td>102.676379</td>\n",
       "      <td>69.775570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>289.421362</td>\n",
       "      <td>182.059312</td>\n",
       "      <td>106.353209</td>\n",
       "      <td>78.935409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AVG_DOWNHOLE_PRESSURE  AVG_DP_TUBING  AVG_DOWNHOLE_TEMPERATURE  \\\n",
       "4              310.376140     277.278260                 96.875890   \n",
       "5              303.500780     281.447440                 96.923390   \n",
       "6              303.534810     276.032000                 96.958850   \n",
       "7              303.782280     282.786760                 96.968730   \n",
       "8              303.858210     289.940670                 97.021360   \n",
       "9              303.791870     299.671930                 97.065690   \n",
       "10             304.335180     282.901000                 96.919460   \n",
       "11             304.848590     273.700730                 96.720340   \n",
       "12             305.371490     259.619930                 96.615630   \n",
       "13             313.870580     282.814360                 96.559530   \n",
       "14             301.375641     204.795183                102.676379   \n",
       "15             289.421362     182.059312                106.353209   \n",
       "\n",
       "    DP_CHOKE_SIZE  \n",
       "4       33.071950  \n",
       "5       22.053340  \n",
       "6       16.163260  \n",
       "7       20.737120  \n",
       "8       12.181530  \n",
       "9        1.490200  \n",
       "10      18.794840  \n",
       "11      28.502580  \n",
       "12      43.157100  \n",
       "13      28.484020  \n",
       "14      69.775570  \n",
       "15      78.935409  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop outliers NB: Visualize using scatter before you decide which to drop\n",
    "dff1P.drop([0],axis = 0, inplace = True)\n",
    "dff1P.drop([745],axis = 0, inplace = True)\n",
    "dff1P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set a particular column as index if you want\n",
    "#dff1P.set_index('AVG_DP_TUBING',inplace = True)\n",
    "#dff1P.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kw/VYMEVp1J+kccBbyfpZrgeOTNW6HwtKx4gjgevK/vjqn4H2fNfLC7gI2Ah0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\n/wcUYXQR/XZLWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23754ef7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SactterPlots\n",
    "#dff3 = dff1P.loc[:,['AVG_DP_TUBING','AVG_DOWNHOLE_PRESSURE']]\n",
    "dff1P.loc[:,['AVG_DP_TUBING','AVG_DOWNHOLE_PRESSURE']].plot(kind = 'scatter', x = 'AVG_DP_TUBING', y = 'AVG_DOWNHOLE_PRESSURE')\n",
    "#dff1P.plot(dff1P.loc[:,['AVG_DP_TUBING']], dff1P.loc[:,['AVG_DOWNHOLE_PRESSURE']])\n",
    "plt.show()\n",
    "#dff3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vn0g6u3ccHuDbwFJlMwL+o6Q5OeMZSQw2Ru059C5mQNak9OW0fGlaX0O6e5B0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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x286ca62b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ScatterPlots\n",
    "#dff3 = dff1P.loc[:,['AVG_DP_TUBING','AVG_DOWNHOLE_PRESSURE']]\n",
    "dff1P.loc[:,['AVG_DOWNHOLE_PRESSURE','DP_CHOKE_SIZE']].plot(kind = 'scatter', x = 'AVG_DOWNHOLE_PRESSURE', y = 'DP_CHOKE_SIZE')\n",
    "#dff1P.plot(dff1P.loc[:,['AVG_DP_TUBING']], dff1P.loc[:,['AVG_DOWNHOLE_PRESSURE']])\n",
    "plt.show()\n",
    "#dff3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hu8PYNrffOOCF7k4QEZcDlwN0dnb6iXYzszoqShzb1eCatwAnABel77Nz5Z+XdC2wJ/C6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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a428fa22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RegressionLinePlots\n",
    "#dff3 = dff1P.loc[:,['AVG_DP_TUBING','AVG_DOWNHOLE_PRESSURE']]\n",
    "dff1P.loc[:,['AVG_DOWNHOLE_PRESSURE','AVG_DOWNHOLE_TEMPERATURE']].plot(kind = 'scatter', x = 'AVG_DOWNHOLE_PRESSURE', y = 'AVG_DOWNHOLE_TEMPERATURE')\n",
    "#dff1P.plot(dff1P.loc[:,['AVG_DP_TUBING']], dff1P.loc[:,['AVG_DOWNHOLE_PRESSURE']])\n",
    "plt.show()\n",
    "#dff3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g1dQYY6Nn+Kpt37597NmzZ6PHAK4Dtm3bltNOOy3HH3/8lct27dqVk08+OW9729s2cDLg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\nlIgRk8cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f323053a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bx plot for each column\n",
    "dff1P.loc[:,['AVG_DOWNHOLE_PRESSURE']].plot(kind = 'box', figsize=(10,20))\n",
    "#ff1P.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Average Downhole Pressure')\n",
    "plt.ylabel('Average Downhole pressure')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2881: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f3231c2da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram for each column\n",
    "plt.figure(figsize = (27,12))\n",
    "ax = plt.gca()\n",
    "dff1P.hist(ax = ax, bins = 100)\n",
    "plt.show()\n",
    "#plt.plot(kind = 'scatter', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Now work on the monthly production dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wellbore name</th>\n",
       "      <th>NPDCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>On Stream</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "      <th>Water</th>\n",
       "      <th>GI</th>\n",
       "      <th>WI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hrs</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>227.5</td>\n",
       "      <td>11142.5</td>\n",
       "      <td>1.59794e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>733.833</td>\n",
       "      <td>24902</td>\n",
       "      <td>3.49623e+06</td>\n",
       "      <td>783.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>705.917</td>\n",
       "      <td>19617.8</td>\n",
       "      <td>2.88666e+06</td>\n",
       "      <td>2068.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>742.417</td>\n",
       "      <td>15085.7</td>\n",
       "      <td>2.24937e+06</td>\n",
       "      <td>6243.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wellbore name  NPDCode    Year  Month On Stream      Oil          Gas  \\\n",
       "0           NaN      NaN     NaN    NaN       hrs      Sm3          Sm3   \n",
       "1    15/9-F-1 C   7405.0  2014.0    4.0     227.5  11142.5  1.59794e+06   \n",
       "2    15/9-F-1 C   7405.0  2014.0    5.0   733.833    24902  3.49623e+06   \n",
       "3    15/9-F-1 C   7405.0  2014.0    6.0   705.917  19617.8  2.88666e+06   \n",
       "4    15/9-F-1 C   7405.0  2014.0    7.0   742.417  15085.7  2.24937e+06   \n",
       "\n",
       "     Water   GI   WI  \n",
       "0      Sm3  Sm3  Sm3  \n",
       "1        0  NaN  NaN  \n",
       "2   783.48  NaN  NaN  \n",
       "3  2068.48  NaN  NaN  \n",
       "4  6243.98  NaN  NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(527, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df_Month.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wellbore name      1\n",
       "NPDCode            1\n",
       "Year               1\n",
       "Month              1\n",
       "On Stream         11\n",
       "Oil              215\n",
       "Gas              215\n",
       "Water            215\n",
       "GI               526\n",
       "WI               325\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Month.isnull().sum()##find out the number of null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wellbore name</th>\n",
       "      <th>NPDCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>On Stream</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hrs</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "      <td>Sm3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>227.5</td>\n",
       "      <td>11142.5</td>\n",
       "      <td>1.59794e+06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>733.833</td>\n",
       "      <td>24902</td>\n",
       "      <td>3.49623e+06</td>\n",
       "      <td>783.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>705.917</td>\n",
       "      <td>19617.8</td>\n",
       "      <td>2.88666e+06</td>\n",
       "      <td>2068.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>742.417</td>\n",
       "      <td>15085.7</td>\n",
       "      <td>2.24937e+06</td>\n",
       "      <td>6243.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wellbore name  NPDCode    Year  Month On Stream      Oil          Gas  \\\n",
       "0           NaN      NaN     NaN    NaN       hrs      Sm3          Sm3   \n",
       "1    15/9-F-1 C   7405.0  2014.0    4.0     227.5  11142.5  1.59794e+06   \n",
       "2    15/9-F-1 C   7405.0  2014.0    5.0   733.833    24902  3.49623e+06   \n",
       "3    15/9-F-1 C   7405.0  2014.0    6.0   705.917  19617.8  2.88666e+06   \n",
       "4    15/9-F-1 C   7405.0  2014.0    7.0   742.417  15085.7  2.24937e+06   \n",
       "\n",
       "     Water  \n",
       "0      Sm3  \n",
       "1        0  \n",
       "2   783.48  \n",
       "3  2068.48  \n",
       "4  6243.98  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Drop the unnecessary columns/select the columns needed\n",
    "dff_M = df_Month.drop(['GI','WI'], axis = 1, inplace = False)\n",
    "dff_M.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wellbore name</th>\n",
       "      <th>NPDCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>On Stream</th>\n",
       "      <th>Oil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>227.5</td>\n",
       "      <td>11142.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>733.833</td>\n",
       "      <td>24902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>705.917</td>\n",
       "      <td>19617.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>742.417</td>\n",
       "      <td>15085.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>432.992</td>\n",
       "      <td>6970.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wellbore name  NPDCode    Year  Month On Stream      Oil\n",
       "1    15/9-F-1 C   7405.0  2014.0    4.0     227.5  11142.5\n",
       "2    15/9-F-1 C   7405.0  2014.0    5.0   733.833    24902\n",
       "3    15/9-F-1 C   7405.0  2014.0    6.0   705.917  19617.8\n",
       "4    15/9-F-1 C   7405.0  2014.0    7.0   742.417  15085.7\n",
       "5    15/9-F-1 C   7405.0  2014.0    8.0   432.992  6970.43"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Work on the oil production \n",
    "##Remove the rows with nan values in the Monthly oil production. Also remove the Gas and Water columns.\n",
    "dff_MO = dff_M.drop(['Gas','Water'],axis = 1, inplace = False)\n",
    "dff_MO.dropna(inplace = True)#Drop nan values in dff_MO\n",
    "dff_MO.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the welbore name as the index to help pull out certain well bore. #reset if you want\n",
    "dff_MO.reset_index('Wellbore name',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wellbore name</th>\n",
       "      <th>NPDCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>On Stream</th>\n",
       "      <th>Oil</th>\n",
       "      <th>day</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>227.5</td>\n",
       "      <td>11142.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>733.833</td>\n",
       "      <td>24902</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>705.917</td>\n",
       "      <td>19617.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>742.417</td>\n",
       "      <td>15085.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>7405.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>432.992</td>\n",
       "      <td>6970.43</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wellbore name  NPDCode    Year  Month On Stream      Oil  day       Date\n",
       "1    15/9-F-1 C   7405.0  2014.0    4.0     227.5  11142.5    1 2014-04-01\n",
       "2    15/9-F-1 C   7405.0  2014.0    5.0   733.833    24902    1 2014-05-01\n",
       "3    15/9-F-1 C   7405.0  2014.0    6.0   705.917  19617.8    1 2014-06-01\n",
       "4    15/9-F-1 C   7405.0  2014.0    7.0   742.417  15085.7    1 2014-07-01\n",
       "5    15/9-F-1 C   7405.0  2014.0    8.0   432.992  6970.43    1 2014-08-01"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff_MO.head()##Just in case you want to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dffM1.head()##Just in case you want to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 6)\n"
     ]
    }
   ],
   "source": [
    "print(dffM1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chiji\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "dffM1['On Stream'] = dffM1['On Stream'].astype(int) #convert column type to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chiji\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import calendar\n",
    "dffM1['Month'] = dffM1['Month'].apply(lambda x: calendar.month_abbr[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dff_MO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff_MO = dff_MO.assign(day = 1)#Just to assign a day column in the dataframe\n",
    "dff_MO['Date'] = pd.to_datetime(dff_MO.loc[:,['Year','Month','day']])#Create a column of date coombining year month and day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffM12 = dffM1.loc[:,['Oil','On Stream','Date']]#Select columns of interest\n",
    "dffM12.set_index('Date', inplace = True)#set date as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffM12.rename(columns ={'On Stream':'On Stream Hours'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dffM12 = dffM1.loc[:,['Oil','On Stream']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dffM12.set_index('Month', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffM12.index = dffM12.index.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from collections import namedtuple\n",
    "#MyDate = namedtuple()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dffM12\n",
    "##Take a look as the time series variation of oil production\n",
    "##Find out if there is a trend/repeated pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dffM12.plot(kind = 'line', figsize = (20,15))\n",
    "plt.ylabel('Volume of oil [barrels]',fontsize = 25)\n",
    "plt.xlabel('Dates',fontsize = 25)\n",
    "#plt.legend(['Views added'])\n",
    "plt.rc('font',size = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Take a separate look at the different wells "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff_MO = dff_MO.loc[:,['Wellbore name','NPDCode','Oil','Date']]#Select columns of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "WellC_list = dff_MO['Wellbore name'].unique().tolist()#Get the list of wells in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1944x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1944x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1944x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1944x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1944x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1944x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = {}\n",
    "for i in WellC_list:\n",
    "    hy = i\n",
    "    df[hy] = dff_MO.loc[dff_MO['Wellbore name'] == i]\n",
    "    df[hy] = df[hy].loc[:,['Oil','Date']].set_index('Date',inplace = False)\n",
    "    plt.figure(figsize=(27, 12))\n",
    "    #plt.subplot(len(gy),1,j+1)\n",
    "    df[hy].plot(kind = 'line')\n",
    "    plt.ylabel('Oil production [Bbl]',fontsize = 25)\n",
    "    plt.xlabel('Dates',fontsize = 25)\n",
    "    plt.legend([hy])\n",
    "plt.rc('font',size = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2160x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 15))\n",
    "df['15/9-F-12'].plot(kind = 'line')\n",
    "plt.ylabel('Oil production [Bbl]',fontsize = 15)\n",
    "plt.xlabel('Dates',fontsize = 5)\n",
    "plt.legend(['15/9-F-12'])\n",
    "#plt.rc('font',size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(30, 15))\n",
    "##Blow out the well '15/9-F-12'\n",
    "df['15/9-F-12'].plot(kind = 'line',figsize=(14, 8))\n",
    "plt.ylabel('Oil production [Bbl]',fontsize = 15)\n",
    "plt.xlabel('Dates',fontsize = 15)\n",
    "plt.legend(['15/9-F-12'])\n",
    "#plt.rc('font',size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['15/9-F-1 C',\n",
       " '15/9-F-11',\n",
       " '15/9-F-12',\n",
       " '15/9-F-14',\n",
       " '15/9-F-15 D',\n",
       " '15/9-F-5']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Well_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do this visualization for all the well bore\n",
    "df_(15/9-F-1 C) = dff_MO.loc[dff_MO['Wellbore name'] == '15/9-F-1 C']\n",
    "dffM1 = dff_MO.loc[dff_MO['Wellbore name'] == '15/9-F-1 C']\n",
    "dffM1 = dff_MO.loc[dff_MO['Wellbore name'] == '15/9-F-1 C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = 're'\n",
    "gy = u+'r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rer'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Oil production')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#On stream hours vs Oil production\n",
    "plt.scatter(x = dffM12['On Stream'],y = dffM12['Oil'])\n",
    "plt.title('Oil production vs On stream hours')\n",
    "plt.xlabel('On Stream')\n",
    "plt.ylabel('Oil production')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8c9a0aeb8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dffM12.plot(kind = 'area', x = 'On Stream', y = 'Oil')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffM12 = dffM1.loc[:,['Oil','Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffM12.set_index('Year', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d8c9b0e438>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dffM12.plot(kind = 'line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "##convert the year and month into datetime and set it as the index\n",
    "#dffM1['Date'] = pd.to_datetime(dffM1[['Year','Month']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2,)\n",
      "(15634, 24)\n",
      "0        production\n",
      "1        production\n",
      "2        production\n",
      "3        production\n",
      "4        production\n",
      "5        production\n",
      "6        production\n",
      "7        production\n",
      "8        production\n",
      "9        production\n",
      "10       production\n",
      "11       production\n",
      "12       production\n",
      "13       production\n",
      "14       production\n",
      "15       production\n",
      "16       production\n",
      "17       production\n",
      "18       production\n",
      "19       production\n",
      "20       production\n",
      "21       production\n",
      "22       production\n",
      "23       production\n",
      "24       production\n",
      "25       production\n",
      "26       production\n",
      "27       production\n",
      "28       production\n",
      "29       production\n",
      "            ...    \n",
      "15604    production\n",
      "15605    production\n",
      "15606    production\n",
      "15607    production\n",
      "15608    production\n",
      "15609    production\n",
      "15610    production\n",
      "15611    production\n",
      "15612    production\n",
      "15613    production\n",
      "15614    production\n",
      "15615    production\n",
      "15616    production\n",
      "15617    production\n",
      "15618    production\n",
      "15619    production\n",
      "15620    production\n",
      "15621    production\n",
      "15622    production\n",
      "15623    production\n",
      "15624    production\n",
      "15625    production\n",
      "15626    production\n",
      "15627    production\n",
      "15628    production\n",
      "15629    production\n",
      "15630    production\n",
      "15631    production\n",
      "15632    production\n",
      "15633     injection\n",
      "Name: FLOW_KIND, Length: 15634, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Explore the different columns and find out the unique memebers.\n",
    "#results show 7 different wellbore id, codes, and names\n",
    "ud = df.iloc[:,22].unique()\n",
    "print(ud.shape)\n",
    "print(df.shape)\n",
    "print(df.iloc[:,22])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATEPRD</th>\n",
       "      <th>NPD_WELL_BORE_NAME</th>\n",
       "      <th>NPD_FIELD_NAME</th>\n",
       "      <th>NPD_FACILITY_NAME</th>\n",
       "      <th>ON_STREAM_HRS</th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
       "      <th>AVG_DP_TUBING</th>\n",
       "      <th>AVG_ANNULUS_PRESS</th>\n",
       "      <th>WELL_BORE_CODE</th>\n",
       "      <th>AVG_CHOKE_UOM</th>\n",
       "      <th>AVG_WHP_P</th>\n",
       "      <th>AVG_WHT_P</th>\n",
       "      <th>DP_CHOKE_SIZE</th>\n",
       "      <th>BORE_OIL_VOL</th>\n",
       "      <th>BORE_GAS_VOL</th>\n",
       "      <th>BORE_WAT_VOL</th>\n",
       "      <th>BORE_WI_VOL</th>\n",
       "      <th>FLOW_KIND</th>\n",
       "      <th>WELL_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-04-07</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-08</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-09</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-10</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>%</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.37614</td>\n",
       "      <td>96.87589</td>\n",
       "      <td>277.27826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>%</td>\n",
       "      <td>33.09788</td>\n",
       "      <td>10.47992</td>\n",
       "      <td>33.07195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DATEPRD NPD_WELL_BORE_NAME NPD_FIELD_NAME NPD_FACILITY_NAME  \\\n",
       "0 2014-04-07         15/9-F-1 C          VOLVE    MÆRSK INSPIRER   \n",
       "1 2014-04-08         15/9-F-1 C          VOLVE    MÆRSK INSPIRER   \n",
       "2 2014-04-09         15/9-F-1 C          VOLVE    MÆRSK INSPIRER   \n",
       "3 2014-04-10         15/9-F-1 C          VOLVE    MÆRSK INSPIRER   \n",
       "4 2014-04-11         15/9-F-1 C          VOLVE    MÆRSK INSPIRER   \n",
       "\n",
       "   ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  AVG_DOWNHOLE_TEMPERATURE  \\\n",
       "0            0.0                0.00000                   0.00000   \n",
       "1            0.0                    NaN                       NaN   \n",
       "2            0.0                    NaN                       NaN   \n",
       "3            0.0                    NaN                       NaN   \n",
       "4            0.0              310.37614                  96.87589   \n",
       "\n",
       "   AVG_DP_TUBING  AVG_ANNULUS_PRESS WELL_BORE_CODE AVG_CHOKE_UOM  AVG_WHP_P  \\\n",
       "0        0.00000                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "1            NaN                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "2            NaN                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "3            NaN                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "4      277.27826                0.0  NO 15/9-F-1 C             %   33.09788   \n",
       "\n",
       "   AVG_WHT_P  DP_CHOKE_SIZE  BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL  \\\n",
       "0    0.00000        0.00000           0.0           0.0           0.0   \n",
       "1    0.00000        0.00000           0.0           0.0           0.0   \n",
       "2    0.00000        0.00000           0.0           0.0           0.0   \n",
       "3    0.00000        0.00000           0.0           0.0           0.0   \n",
       "4   10.47992       33.07195           0.0           0.0           0.0   \n",
       "\n",
       "   BORE_WI_VOL   FLOW_KIND WELL_TYPE  \n",
       "0          NaN  production        WI  \n",
       "1          NaN  production        OP  \n",
       "2          NaN  production        OP  \n",
       "3          NaN  production        OP  \n",
       "4          NaN  production        OP  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NewDF = df.iloc[:,[0,3,5,7,8,9,10,11,12,1,14,15,16,17,18,19,20,21,22,23]]\n",
    "NewDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:,[8,9,10,11,12,1,14,15,16,17,18,19,20,21,22,23]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ON_STREAM_HRS</th>\n",
       "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
       "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
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      "text/plain": [
       "   ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  AVG_DOWNHOLE_TEMPERATURE  \\\n",
       "0            0.0                0.00000                   0.00000   \n",
       "1            0.0                    NaN                       NaN   \n",
       "2            0.0                    NaN                       NaN   \n",
       "3            0.0                    NaN                       NaN   \n",
       "4            0.0              310.37614                  96.87589   \n",
       "\n",
       "   AVG_DP_TUBING  AVG_ANNULUS_PRESS WELL_BORE_CODE AVG_CHOKE_UOM  AVG_WHP_P  \\\n",
       "0        0.00000                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "1            NaN                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "2            NaN                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "3            NaN                0.0  NO 15/9-F-1 C             %    0.00000   \n",
       "4      277.27826                0.0  NO 15/9-F-1 C             %   33.09788   \n",
       "\n",
       "   AVG_WHT_P  DP_CHOKE_SIZE  BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL  \\\n",
       "0    0.00000        0.00000           0.0           0.0           0.0   \n",
       "1    0.00000        0.00000           0.0           0.0           0.0   \n",
       "2    0.00000        0.00000           0.0           0.0           0.0   \n",
       "3    0.00000        0.00000           0.0           0.0           0.0   \n",
       "4   10.47992       33.07195           0.0           0.0           0.0   \n",
       "\n",
       "   BORE_WI_VOL   FLOW_KIND WELL_TYPE  \n",
       "0          NaN  production        WI  \n",
       "1          NaN  production        OP  \n",
       "2          NaN  production        OP  \n",
       "3          NaN  production        OP  \n",
       "4          NaN  production        OP  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  AVG_DOWNHOLE_TEMPERATURE  \\\n",
      "ON_STREAM_HRS                     1.000                 -0.003                     0.106   \n",
      "AVG_DOWNHOLE_PRESSURE            -0.003                  1.000                     0.968   \n",
      "AVG_DOWNHOLE_TEMPERATURE          0.106                  0.968                     1.000   \n",
      "AVG_DP_TUBING                     0.003                  0.950                     0.899   \n",
      "AVG_ANNULUS_PRESS                 0.483                 -0.124                    -0.087   \n",
      "AVG_WHP_P                        -0.045                  0.283                     0.274   \n",
      "AVG_WHT_P                         0.763                 -0.095                    -0.077   \n",
      "DP_CHOKE_SIZE                    -0.229                  0.268                     0.222   \n",
      "BORE_OIL_VOL                      0.342                  0.249                     0.290   \n",
      "BORE_GAS_VOL                      0.354                  0.246                     0.287   \n",
      "BORE_WAT_VOL                      0.405                 -0.297                    -0.344   \n",
      "BORE_WI_VOL                       0.750                    NaN                       NaN   \n",
      "\n",
      "                          AVG_DP_TUBING  AVG_ANNULUS_PRESS  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE  \\\n",
      "ON_STREAM_HRS                     0.003              0.483     -0.045      0.763         -0.229   \n",
      "AVG_DOWNHOLE_PRESSURE             0.950             -0.124      0.283     -0.095          0.268   \n",
      "AVG_DOWNHOLE_TEMPERATURE          0.899             -0.087      0.274     -0.077          0.222   \n",
      "AVG_DP_TUBING                     1.000             -0.110      0.102     -0.054          0.093   \n",
      "AVG_ANNULUS_PRESS                -0.110              1.000     -0.003      0.316         -0.178   \n",
      "AVG_WHP_P                         0.102             -0.003      1.000     -0.041          0.909   \n",
      "AVG_WHT_P                        -0.054              0.316     -0.041      1.000         -0.349   \n",
      "DP_CHOKE_SIZE                     0.093             -0.178      0.909     -0.349          1.000   \n",
      "BORE_OIL_VOL                      0.127             -0.025      0.430      0.382          0.288   \n",
      "BORE_GAS_VOL                      0.125             -0.028      0.429      0.396          0.282   \n",
      "BORE_WAT_VOL                     -0.172              0.094     -0.329      0.684         -0.494   \n",
      "BORE_WI_VOL                         NaN                NaN        NaN        NaN         -0.100   \n",
      "\n",
      "                          BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL  BORE_WI_VOL  \n",
      "ON_STREAM_HRS                    0.342         0.354         0.405         0.75  \n",
      "AVG_DOWNHOLE_PRESSURE            0.249         0.246        -0.297          NaN  \n",
      "AVG_DOWNHOLE_TEMPERATURE         0.290         0.287        -0.344          NaN  \n",
      "AVG_DP_TUBING                    0.127         0.125        -0.172          NaN  \n",
      "AVG_ANNULUS_PRESS               -0.025        -0.028         0.094          NaN  \n",
      "AVG_WHP_P                        0.430         0.429        -0.329          NaN  \n",
      "AVG_WHT_P                        0.382         0.396         0.684          NaN  \n",
      "DP_CHOKE_SIZE                    0.288         0.282        -0.494        -0.10  \n",
      "BORE_OIL_VOL                     1.000         0.999        -0.113          NaN  \n",
      "BORE_GAS_VOL                     0.999         1.000        -0.096          NaN  \n",
      "BORE_WAT_VOL                    -0.113        -0.096         1.000          NaN  \n",
      "BORE_WI_VOL                        NaN           NaN           NaN         1.00  \n"
     ]
    }
   ],
   "source": [
    "# What is the correlation between variables?\n",
    "from pandas import set_option\n",
    "set_option('display.width', 100)\n",
    "set_option('precision', 3)\n",
    "correlations = X.corr(method='pearson')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATEPRD'] = pd.to_datetime(df['DATEPRD'], format='%y.%d.%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DATEPRD</th>\n",
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       "      <th>FLOW_KIND</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2014-04-07</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-10</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-11</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>310.376</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>33.098</td>\n",
       "      <td>10.480</td>\n",
       "      <td>33.072</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-04-12</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>303.501</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>22.053</td>\n",
       "      <td>8.704</td>\n",
       "      <td>22.053</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-04-13</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>303.535</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>27.503</td>\n",
       "      <td>9.423</td>\n",
       "      <td>16.163</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-04-14</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>303.782</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>20.996</td>\n",
       "      <td>8.131</td>\n",
       "      <td>20.737</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-04-15</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>303.858</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>13.918</td>\n",
       "      <td>8.498</td>\n",
       "      <td>12.182</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-04-16</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>303.792</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>4.120</td>\n",
       "      <td>8.821</td>\n",
       "      <td>1.490</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-04-17</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>304.335</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>21.434</td>\n",
       "      <td>8.854</td>\n",
       "      <td>18.795</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>304.849</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>31.148</td>\n",
       "      <td>9.640</td>\n",
       "      <td>28.503</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-04-19</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>305.371</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>45.752</td>\n",
       "      <td>9.639</td>\n",
       "      <td>43.157</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-04-20</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>313.871</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>31.056</td>\n",
       "      <td>9.620</td>\n",
       "      <td>28.484</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-04-21</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>11.500</td>\n",
       "      <td>301.376</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>96.580</td>\n",
       "      <td>19.197</td>\n",
       "      <td>69.776</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>289.421</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>107.362</td>\n",
       "      <td>37.939</td>\n",
       "      <td>78.935</td>\n",
       "      <td>631.47</td>\n",
       "      <td>90439.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-04-23</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>270.240</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>99.187</td>\n",
       "      <td>60.757</td>\n",
       "      <td>70.627</td>\n",
       "      <td>1166.46</td>\n",
       "      <td>165720.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-04-24</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>262.843</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>94.601</td>\n",
       "      <td>63.047</td>\n",
       "      <td>66.049</td>\n",
       "      <td>1549.81</td>\n",
       "      <td>221707.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-04-25</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>255.527</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>89.988</td>\n",
       "      <td>64.547</td>\n",
       "      <td>61.405</td>\n",
       "      <td>1248.70</td>\n",
       "      <td>178063.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-04-26</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>247.199</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>84.777</td>\n",
       "      <td>65.724</td>\n",
       "      <td>56.148</td>\n",
       "      <td>1345.78</td>\n",
       "      <td>192602.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-04-27</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>240.736</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>80.837</td>\n",
       "      <td>66.934</td>\n",
       "      <td>52.202</td>\n",
       "      <td>1349.56</td>\n",
       "      <td>194496.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-04-28</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>235.021</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>77.338</td>\n",
       "      <td>67.848</td>\n",
       "      <td>48.708</td>\n",
       "      <td>1345.61</td>\n",
       "      <td>192899.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-04-29</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>232.744</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>75.949</td>\n",
       "      <td>65.707</td>\n",
       "      <td>47.376</td>\n",
       "      <td>1279.46</td>\n",
       "      <td>184900.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>233.298</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>76.118</td>\n",
       "      <td>62.796</td>\n",
       "      <td>47.611</td>\n",
       "      <td>1225.62</td>\n",
       "      <td>177107.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-05-01</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>231.089</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>74.690</td>\n",
       "      <td>62.377</td>\n",
       "      <td>46.196</td>\n",
       "      <td>1212.90</td>\n",
       "      <td>174608.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-05-02</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>231.419</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>74.729</td>\n",
       "      <td>60.747</td>\n",
       "      <td>46.150</td>\n",
       "      <td>1074.04</td>\n",
       "      <td>154870.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-05-03</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>230.766</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>74.160</td>\n",
       "      <td>60.628</td>\n",
       "      <td>45.733</td>\n",
       "      <td>1044.17</td>\n",
       "      <td>147929.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-05-04</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>230.596</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>73.908</td>\n",
       "      <td>58.279</td>\n",
       "      <td>45.491</td>\n",
       "      <td>970.20</td>\n",
       "      <td>139378.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>229.424</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>72.994</td>\n",
       "      <td>54.905</td>\n",
       "      <td>44.549</td>\n",
       "      <td>946.12</td>\n",
       "      <td>136980.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-05-06</td>\n",
       "      <td>NO 15/9-F-1 C</td>\n",
       "      <td>7405</td>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>225.902</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>70.952</td>\n",
       "      <td>58.883</td>\n",
       "      <td>42.450</td>\n",
       "      <td>1002.83</td>\n",
       "      <td>144950.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15604</th>\n",
       "      <td>2016-08-20</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.798</td>\n",
       "      <td>52.077</td>\n",
       "      <td>0.255</td>\n",
       "      <td>355.95</td>\n",
       "      <td>56336.02</td>\n",
       "      <td>90.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15605</th>\n",
       "      <td>2016-08-21</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.796</td>\n",
       "      <td>53.197</td>\n",
       "      <td>0.254</td>\n",
       "      <td>369.99</td>\n",
       "      <td>57866.64</td>\n",
       "      <td>91.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15606</th>\n",
       "      <td>2016-08-22</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.650</td>\n",
       "      <td>57.480</td>\n",
       "      <td>0.120</td>\n",
       "      <td>371.33</td>\n",
       "      <td>57934.84</td>\n",
       "      <td>90.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15607</th>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.728</td>\n",
       "      <td>56.862</td>\n",
       "      <td>0.190</td>\n",
       "      <td>378.55</td>\n",
       "      <td>59119.57</td>\n",
       "      <td>90.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15608</th>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.703</td>\n",
       "      <td>56.249</td>\n",
       "      <td>0.168</td>\n",
       "      <td>376.86</td>\n",
       "      <td>58632.31</td>\n",
       "      <td>91.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>24.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.642</td>\n",
       "      <td>57.884</td>\n",
       "      <td>0.102</td>\n",
       "      <td>377.44</td>\n",
       "      <td>59337.43</td>\n",
       "      <td>91.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>20.592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>23.970</td>\n",
       "      <td>55.872</td>\n",
       "      <td>0.420</td>\n",
       "      <td>327.16</td>\n",
       "      <td>52209.60</td>\n",
       "      <td>77.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15611</th>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>12.679</td>\n",
       "      <td>33.755</td>\n",
       "      <td>7.620</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>2016-08-28</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>1.180</td>\n",
       "      <td>18.830</td>\n",
       "      <td>1.180</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>2016-08-29</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>52.031</td>\n",
       "      <td>17.527</td>\n",
       "      <td>52.031</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>83.865</td>\n",
       "      <td>12.277</td>\n",
       "      <td>83.865</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15615</th>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>6.009</td>\n",
       "      <td>2.298</td>\n",
       "      <td>6.009</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15616</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>6.002</td>\n",
       "      <td>2.297</td>\n",
       "      <td>6.002</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15617</th>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>6.003</td>\n",
       "      <td>2.297</td>\n",
       "      <td>6.003</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15618</th>\n",
       "      <td>2016-09-03</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>2.891</td>\n",
       "      <td>1.211</td>\n",
       "      <td>2.891</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15619</th>\n",
       "      <td>2016-09-04</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15620</th>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15621</th>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15622</th>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15623</th>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15624</th>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15625</th>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15626</th>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15627</th>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>production</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15628</th>\n",
       "      <td>2016-09-13</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15629</th>\n",
       "      <td>2016-09-14</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15630</th>\n",
       "      <td>2016-09-15</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15631</th>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15632</th>\n",
       "      <td>2016-09-17</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>%</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production</td>\n",
       "      <td>OP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15633</th>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>NO 15/9-F-5 AH</td>\n",
       "      <td>5769</td>\n",
       "      <td>15/9-F-5</td>\n",
       "      <td>3420717</td>\n",
       "      <td>VOLVE</td>\n",
       "      <td>369304</td>\n",
       "      <td>MÆRSK INSPIRER</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>injection</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15634 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATEPRD  WELL_BORE_CODE  NPD_WELL_BORE_CODE NPD_WELL_BORE_NAME  NPD_FIELD_CODE  \\\n",
       "0     2014-04-07   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "1     2014-04-08   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "2     2014-04-09   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "3     2014-04-10   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "4     2014-04-11   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "5     2014-04-12   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "6     2014-04-13   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "7     2014-04-14   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "8     2014-04-15   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "9     2014-04-16   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "10    2014-04-17   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "11    2014-04-18   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "12    2014-04-19   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "13    2014-04-20   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "14    2014-04-21   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "15    2014-04-22   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "16    2014-04-23   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "17    2014-04-24   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "18    2014-04-25   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "19    2014-04-26   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "20    2014-04-27   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "21    2014-04-28   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "22    2014-04-29   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "23    2014-04-30   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "24    2014-05-01   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "25    2014-05-02   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "26    2014-05-03   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "27    2014-05-04   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "28    2014-05-05   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "29    2014-05-06   NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
       "...          ...             ...                 ...                ...             ...   \n",
       "15604 2016-08-20  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15605 2016-08-21  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15606 2016-08-22  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15607 2016-08-23  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15608 2016-08-24  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15609 2016-08-25  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15610 2016-08-26  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15611 2016-08-27  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15612 2016-08-28  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15613 2016-08-29  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15614 2016-08-30  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15615 2016-08-31  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15616 2016-09-01  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15617 2016-09-02  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15618 2016-09-03  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15619 2016-09-04  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15620 2016-09-05  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15621 2016-09-06  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15622 2016-09-07  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15623 2016-09-08  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15624 2016-09-09  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15625 2016-09-10  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15626 2016-09-11  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15627 2016-09-12  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15628 2016-09-13  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15629 2016-09-14  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15630 2016-09-15  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15631 2016-09-16  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15632 2016-09-17  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "15633 2016-09-18  NO 15/9-F-5 AH                5769           15/9-F-5         3420717   \n",
       "\n",
       "      NPD_FIELD_NAME  NPD_FACILITY_CODE NPD_FACILITY_NAME  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
       "0              VOLVE             369304    MÆRSK INSPIRER          0.000                  0.000   \n",
       "1              VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "2              VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "3              VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "4              VOLVE             369304    MÆRSK INSPIRER          0.000                310.376   \n",
       "5              VOLVE             369304    MÆRSK INSPIRER          0.000                303.501   \n",
       "6              VOLVE             369304    MÆRSK INSPIRER          0.000                303.535   \n",
       "7              VOLVE             369304    MÆRSK INSPIRER          0.000                303.782   \n",
       "8              VOLVE             369304    MÆRSK INSPIRER          0.000                303.858   \n",
       "9              VOLVE             369304    MÆRSK INSPIRER          0.000                303.792   \n",
       "10             VOLVE             369304    MÆRSK INSPIRER          0.000                304.335   \n",
       "11             VOLVE             369304    MÆRSK INSPIRER          0.000                304.849   \n",
       "12             VOLVE             369304    MÆRSK INSPIRER          0.000                305.371   \n",
       "13             VOLVE             369304    MÆRSK INSPIRER          0.000                313.871   \n",
       "14             VOLVE             369304    MÆRSK INSPIRER         11.500                301.376   \n",
       "15             VOLVE             369304    MÆRSK INSPIRER         24.000                289.421   \n",
       "16             VOLVE             369304    MÆRSK INSPIRER         24.000                270.240   \n",
       "17             VOLVE             369304    MÆRSK INSPIRER         24.000                262.843   \n",
       "18             VOLVE             369304    MÆRSK INSPIRER         24.000                255.527   \n",
       "19             VOLVE             369304    MÆRSK INSPIRER         24.000                247.199   \n",
       "20             VOLVE             369304    MÆRSK INSPIRER         24.000                240.736   \n",
       "21             VOLVE             369304    MÆRSK INSPIRER         24.000                235.021   \n",
       "22             VOLVE             369304    MÆRSK INSPIRER         24.000                232.744   \n",
       "23             VOLVE             369304    MÆRSK INSPIRER         24.000                233.298   \n",
       "24             VOLVE             369304    MÆRSK INSPIRER         24.000                231.089   \n",
       "25             VOLVE             369304    MÆRSK INSPIRER         24.000                231.419   \n",
       "26             VOLVE             369304    MÆRSK INSPIRER         24.000                230.766   \n",
       "27             VOLVE             369304    MÆRSK INSPIRER         24.000                230.596   \n",
       "28             VOLVE             369304    MÆRSK INSPIRER         24.000                229.424   \n",
       "29             VOLVE             369304    MÆRSK INSPIRER         24.000                225.902   \n",
       "...              ...                ...               ...            ...                    ...   \n",
       "15604          VOLVE             369304    MÆRSK INSPIRER         24.000                    NaN   \n",
       "15605          VOLVE             369304    MÆRSK INSPIRER         24.000                    NaN   \n",
       "15606          VOLVE             369304    MÆRSK INSPIRER         24.000                    NaN   \n",
       "15607          VOLVE             369304    MÆRSK INSPIRER         24.000                    NaN   \n",
       "15608          VOLVE             369304    MÆRSK INSPIRER         24.000                    NaN   \n",
       "15609          VOLVE             369304    MÆRSK INSPIRER         24.000                    NaN   \n",
       "15610          VOLVE             369304    MÆRSK INSPIRER         20.592                    NaN   \n",
       "15611          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15612          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15613          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15614          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15615          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15616          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15617          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15618          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15619          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15620          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15621          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15622          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15623          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15624          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15625          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15626          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15627          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15628          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15629          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15630          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15631          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15632          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "15633          VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
       "\n",
       "         ...      AVG_CHOKE_UOM  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE BORE_OIL_VOL  BORE_GAS_VOL  \\\n",
       "0        ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
       "1        ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
       "2        ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
       "3        ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
       "4        ...                  %     33.098     10.480         33.072         0.00          0.00   \n",
       "5        ...                  %     22.053      8.704         22.053         0.00          0.00   \n",
       "6        ...                  %     27.503      9.423         16.163         0.00          0.00   \n",
       "7        ...                  %     20.996      8.131         20.737         0.00          0.00   \n",
       "8        ...                  %     13.918      8.498         12.182         0.00          0.00   \n",
       "9        ...                  %      4.120      8.821          1.490         0.00          0.00   \n",
       "10       ...                  %     21.434      8.854         18.795         0.00          0.00   \n",
       "11       ...                  %     31.148      9.640         28.503         0.00          0.00   \n",
       "12       ...                  %     45.752      9.639         43.157         0.00          0.00   \n",
       "13       ...                  %     31.056      9.620         28.484         0.00          0.00   \n",
       "14       ...                  %     96.580     19.197         69.776         0.00          0.00   \n",
       "15       ...                  %    107.362     37.939         78.935       631.47      90439.09   \n",
       "16       ...                  %     99.187     60.757         70.627      1166.46     165720.39   \n",
       "17       ...                  %     94.601     63.047         66.049      1549.81     221707.31   \n",
       "18       ...                  %     89.988     64.547         61.405      1248.70     178063.52   \n",
       "19       ...                  %     84.777     65.724         56.148      1345.78     192602.19   \n",
       "20       ...                  %     80.837     66.934         52.202      1349.56     194496.27   \n",
       "21       ...                  %     77.338     67.848         48.708      1345.61     192899.71   \n",
       "22       ...                  %     75.949     65.707         47.376      1279.46     184900.31   \n",
       "23       ...                  %     76.118     62.796         47.611      1225.62     177107.86   \n",
       "24       ...                  %     74.690     62.377         46.196      1212.90     174608.88   \n",
       "25       ...                  %     74.729     60.747         46.150      1074.04     154870.67   \n",
       "26       ...                  %     74.160     60.628         45.733      1044.17     147929.93   \n",
       "27       ...                  %     73.908     58.279         45.491       970.20     139378.45   \n",
       "28       ...                  %     72.994     54.905         44.549       946.12     136980.49   \n",
       "29       ...                  %     70.952     58.883         42.450      1002.83     144950.15   \n",
       "...      ...                ...        ...        ...            ...          ...           ...   \n",
       "15604    ...                  %     23.798     52.077          0.255       355.95      56336.02   \n",
       "15605    ...                  %     23.796     53.197          0.254       369.99      57866.64   \n",
       "15606    ...                  %     23.650     57.480          0.120       371.33      57934.84   \n",
       "15607    ...                  %     23.728     56.862          0.190       378.55      59119.57   \n",
       "15608    ...                  %     23.703     56.249          0.168       376.86      58632.31   \n",
       "15609    ...                  %     23.642     57.884          0.102       377.44      59337.43   \n",
       "15610    ...                  %     23.970     55.872          0.420       327.16      52209.60   \n",
       "15611    ...                  %     12.679     33.755          7.620         0.00          0.00   \n",
       "15612    ...                  %      1.180     18.830          1.180         0.00          0.00   \n",
       "15613    ...                  %     52.031     17.527         52.031         0.00          0.00   \n",
       "15614    ...                  %     83.865     12.277         83.865         0.00          0.00   \n",
       "15615    ...                  %      6.009      2.298          6.009         0.00          0.00   \n",
       "15616    ...                  %      6.002      2.297          6.002         0.00          0.00   \n",
       "15617    ...                  %      6.003      2.297          6.003         0.00          0.00   \n",
       "15618    ...                  %      2.891      1.211          2.891         0.00          0.00   \n",
       "15619    ...                  %      0.078      0.228          0.078         0.00          0.00   \n",
       "15620    ...                  %      0.085      0.229          0.085         0.00          0.00   \n",
       "15621    ...                  %      0.083      0.229          0.042         0.00          0.00   \n",
       "15622    ...                  %      0.075      0.228          0.038         0.00          0.00   \n",
       "15623    ...                  %      0.085      0.229          0.043         0.00          0.00   \n",
       "15624    ...                  %      0.085      0.229          0.043         0.00          0.00   \n",
       "15625    ...                  %      0.085      0.229          0.040         0.00          0.00   \n",
       "15626    ...                  %      0.087      0.229          0.035         0.00          0.00   \n",
       "15627    ...                  %      0.085      0.229          0.026         0.00          0.00   \n",
       "15628    ...                  %      0.085      0.229          0.037         0.00          0.00   \n",
       "15629    ...                  %      0.078      0.229          0.019         0.00          0.00   \n",
       "15630    ...                  %      0.085      0.229          0.006         0.00          0.00   \n",
       "15631    ...                  %      0.085      0.229          0.012         0.00          0.00   \n",
       "15632    ...                  %      0.075      0.228          0.026         0.00          0.00   \n",
       "15633    ...                NaN        NaN        NaN          0.000          NaN           NaN   \n",
       "\n",
       "       BORE_WAT_VOL  BORE_WI_VOL   FLOW_KIND  WELL_TYPE  \n",
       "0              0.00          NaN  production         WI  \n",
       "1              0.00          NaN  production         OP  \n",
       "2              0.00          NaN  production         OP  \n",
       "3              0.00          NaN  production         OP  \n",
       "4              0.00          NaN  production         OP  \n",
       "5              0.00          NaN  production         OP  \n",
       "6              0.00          NaN  production         OP  \n",
       "7              0.00          NaN  production         OP  \n",
       "8              0.00          NaN  production         OP  \n",
       "9              0.00          NaN  production         OP  \n",
       "10             0.00          NaN  production         OP  \n",
       "11             0.00          NaN  production         OP  \n",
       "12             0.00          NaN  production         OP  \n",
       "13             0.00          NaN  production         OP  \n",
       "14             0.00          NaN  production         OP  \n",
       "15             0.00          NaN  production         OP  \n",
       "16             0.00          NaN  production         OP  \n",
       "17             0.00          NaN  production         OP  \n",
       "18             0.00          NaN  production         OP  \n",
       "19             0.00          NaN  production         OP  \n",
       "20             0.00          NaN  production         OP  \n",
       "21             0.00          NaN  production         OP  \n",
       "22             0.00          NaN  production         OP  \n",
       "23             0.00          NaN  production         OP  \n",
       "24             0.00          NaN  production         OP  \n",
       "25             0.00          NaN  production         OP  \n",
       "26             0.00          NaN  production         OP  \n",
       "27             0.00          NaN  production         OP  \n",
       "28             0.00          NaN  production         OP  \n",
       "29             0.00          NaN  production         OP  \n",
       "...             ...          ...         ...        ...  \n",
       "15604         90.82          NaN  production         OP  \n",
       "15605         91.28          NaN  production         OP  \n",
       "15606         90.61          NaN  production         OP  \n",
       "15607         90.11          NaN  production         OP  \n",
       "15608         91.32          NaN  production         OP  \n",
       "15609         91.38          NaN  production         OP  \n",
       "15610         77.52          NaN  production         OP  \n",
       "15611          0.00          NaN  production         OP  \n",
       "15612          0.00          NaN  production         OP  \n",
       "15613          0.00          NaN  production         OP  \n",
       "15614          0.00          NaN  production         OP  \n",
       "15615          0.00          NaN  production         OP  \n",
       "15616          0.00          NaN  production         OP  \n",
       "15617          0.00          NaN  production         OP  \n",
       "15618          0.00          NaN  production         OP  \n",
       "15619          0.00          NaN  production         OP  \n",
       "15620          0.00          NaN  production         OP  \n",
       "15621          0.00          0.0  production         WI  \n",
       "15622          0.00          0.0  production         WI  \n",
       "15623          0.00          0.0  production         WI  \n",
       "15624          0.00          0.0  production         WI  \n",
       "15625          0.00          0.0  production         WI  \n",
       "15626          0.00          0.0  production         WI  \n",
       "15627          0.00          0.0  production         WI  \n",
       "15628          0.00          NaN  production         OP  \n",
       "15629          0.00          NaN  production         OP  \n",
       "15630          0.00          NaN  production         OP  \n",
       "15631          0.00          NaN  production         OP  \n",
       "15632          0.00          NaN  production         OP  \n",
       "15633           NaN          0.0   injection         WI  \n",
       "\n",
       "[15634 rows x 24 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'NPD_WELL_BORE_NAME'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32mC:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'NPD_WELL_BORE_NAME'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-c94a08c5b857>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m#Extract only one well data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'NPD_WELL_BORE_NAME'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'15/9-F-1 C'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2137\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1840\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1843\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3841\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3842\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3844\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3845\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\HILLARY\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2525\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'NPD_WELL_BORE_NAME'"
     ]
    }
   ],
   "source": [
    "#Extract only one well data\n",
    "X1 = X[X['NPD_WELL_BORE_NAME'] == '15/9-F-1 C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       DATEPRD WELL_BORE_CODE  NPD_WELL_BORE_CODE NPD_WELL_BORE_NAME  NPD_FIELD_CODE  \\\n",
      "0   2014-04-07  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "1   2014-04-08  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "2   2014-04-09  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "3   2014-04-10  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "4   2014-04-11  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "5   2014-04-12  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "6   2014-04-13  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "7   2014-04-14  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "8   2014-04-15  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "9   2014-04-16  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "10  2014-04-17  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "11  2014-04-18  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "12  2014-04-19  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "13  2014-04-20  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "14  2014-04-21  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "15  2014-04-22  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "16  2014-04-23  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "17  2014-04-24  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "18  2014-04-25  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "19  2014-04-26  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "20  2014-04-27  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "21  2014-04-28  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "22  2014-04-29  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "23  2014-04-30  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "24  2014-05-01  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "25  2014-05-02  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "26  2014-05-03  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "27  2014-05-04  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "28  2014-05-05  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "29  2014-05-06  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "..         ...            ...                 ...                ...             ...   \n",
      "716 2016-03-23  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "717 2016-03-24  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "718 2016-03-25  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "719 2016-03-26  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "720 2016-03-27  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "721 2016-03-28  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "722 2016-03-29  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "723 2016-03-30  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "724 2016-03-31  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "725 2016-04-01  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "726 2016-04-02  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "727 2016-04-03  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "728 2016-04-04  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "729 2016-04-05  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "730 2016-04-06  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "731 2016-04-07  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "732 2016-04-08  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "733 2016-04-09  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "734 2016-04-10  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "735 2016-04-11  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "736 2016-04-12  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "737 2016-04-13  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "738 2016-04-14  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "739 2016-04-15  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "740 2016-04-16  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "741 2016-04-17  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "742 2016-04-18  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "743 2016-04-19  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "744 2016-04-20  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "745 2016-04-21  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "\n",
      "    NPD_FIELD_NAME  NPD_FACILITY_CODE NPD_FACILITY_NAME  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
      "0            VOLVE             369304    MÆRSK INSPIRER          0.000                  0.000   \n",
      "1            VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
      "2            VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
      "3            VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
      "4            VOLVE             369304    MÆRSK INSPIRER          0.000                310.376   \n",
      "5            VOLVE             369304    MÆRSK INSPIRER          0.000                303.501   \n",
      "6            VOLVE             369304    MÆRSK INSPIRER          0.000                303.535   \n",
      "7            VOLVE             369304    MÆRSK INSPIRER          0.000                303.782   \n",
      "8            VOLVE             369304    MÆRSK INSPIRER          0.000                303.858   \n",
      "9            VOLVE             369304    MÆRSK INSPIRER          0.000                303.792   \n",
      "10           VOLVE             369304    MÆRSK INSPIRER          0.000                304.335   \n",
      "11           VOLVE             369304    MÆRSK INSPIRER          0.000                304.849   \n",
      "12           VOLVE             369304    MÆRSK INSPIRER          0.000                305.371   \n",
      "13           VOLVE             369304    MÆRSK INSPIRER          0.000                313.871   \n",
      "14           VOLVE             369304    MÆRSK INSPIRER         11.500                301.376   \n",
      "15           VOLVE             369304    MÆRSK INSPIRER         24.000                289.421   \n",
      "16           VOLVE             369304    MÆRSK INSPIRER         24.000                270.240   \n",
      "17           VOLVE             369304    MÆRSK INSPIRER         24.000                262.843   \n",
      "18           VOLVE             369304    MÆRSK INSPIRER         24.000                255.527   \n",
      "19           VOLVE             369304    MÆRSK INSPIRER         24.000                247.199   \n",
      "20           VOLVE             369304    MÆRSK INSPIRER         24.000                240.736   \n",
      "21           VOLVE             369304    MÆRSK INSPIRER         24.000                235.021   \n",
      "22           VOLVE             369304    MÆRSK INSPIRER         24.000                232.744   \n",
      "23           VOLVE             369304    MÆRSK INSPIRER         24.000                233.298   \n",
      "24           VOLVE             369304    MÆRSK INSPIRER         24.000                231.089   \n",
      "25           VOLVE             369304    MÆRSK INSPIRER         24.000                231.419   \n",
      "26           VOLVE             369304    MÆRSK INSPIRER         24.000                230.766   \n",
      "27           VOLVE             369304    MÆRSK INSPIRER         24.000                230.596   \n",
      "28           VOLVE             369304    MÆRSK INSPIRER         24.000                229.424   \n",
      "29           VOLVE             369304    MÆRSK INSPIRER         24.000                225.902   \n",
      "..             ...                ...               ...            ...                    ...   \n",
      "716          VOLVE             369304    MÆRSK INSPIRER         24.000                255.506   \n",
      "717          VOLVE             369304    MÆRSK INSPIRER         24.000                250.393   \n",
      "718          VOLVE             369304    MÆRSK INSPIRER         24.000                246.173   \n",
      "719          VOLVE             369304    MÆRSK INSPIRER         24.000                241.959   \n",
      "720          VOLVE             369304    MÆRSK INSPIRER         23.000                238.132   \n",
      "721          VOLVE             369304    MÆRSK INSPIRER         24.000                235.061   \n",
      "722          VOLVE             369304    MÆRSK INSPIRER         24.000                231.524   \n",
      "723          VOLVE             369304    MÆRSK INSPIRER         24.000                228.853   \n",
      "724          VOLVE             369304    MÆRSK INSPIRER         24.000                226.555   \n",
      "725          VOLVE             369304    MÆRSK INSPIRER         24.000                224.568   \n",
      "726          VOLVE             369304    MÆRSK INSPIRER         24.000                223.012   \n",
      "727          VOLVE             369304    MÆRSK INSPIRER         24.000                221.813   \n",
      "728          VOLVE             369304    MÆRSK INSPIRER         24.000                220.780   \n",
      "729          VOLVE             369304    MÆRSK INSPIRER         24.000                218.752   \n",
      "730          VOLVE             369304    MÆRSK INSPIRER         20.825                218.438   \n",
      "731          VOLVE             369304    MÆRSK INSPIRER          0.000                228.199   \n",
      "732          VOLVE             369304    MÆRSK INSPIRER          0.000                234.980   \n",
      "733          VOLVE             369304    MÆRSK INSPIRER          0.000                239.915   \n",
      "734          VOLVE             369304    MÆRSK INSPIRER          0.000                244.889   \n",
      "735          VOLVE             369304    MÆRSK INSPIRER          0.000                249.408   \n",
      "736          VOLVE             369304    MÆRSK INSPIRER          0.000                253.410   \n",
      "737          VOLVE             369304    MÆRSK INSPIRER          0.000                256.990   \n",
      "738          VOLVE             369304    MÆRSK INSPIRER          0.000                260.284   \n",
      "739          VOLVE             369304    MÆRSK INSPIRER          0.000                263.327   \n",
      "740          VOLVE             369304    MÆRSK INSPIRER          0.000                266.148   \n",
      "741          VOLVE             369304    MÆRSK INSPIRER          0.000                270.268   \n",
      "742          VOLVE             369304    MÆRSK INSPIRER          0.000                276.869   \n",
      "743          VOLVE             369304    MÆRSK INSPIRER          0.000                282.105   \n",
      "744          VOLVE             369304    MÆRSK INSPIRER          0.000                285.096   \n",
      "745          VOLVE             369304    MÆRSK INSPIRER          0.000                  0.000   \n",
      "\n",
      "       ...      AVG_CHOKE_UOM  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE BORE_OIL_VOL  BORE_GAS_VOL  \\\n",
      "0      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "1      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "2      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "3      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "4      ...                  %     33.098     10.480         33.072         0.00          0.00   \n",
      "5      ...                  %     22.053      8.704         22.053         0.00          0.00   \n",
      "6      ...                  %     27.503      9.423         16.163         0.00          0.00   \n",
      "7      ...                  %     20.996      8.131         20.737         0.00          0.00   \n",
      "8      ...                  %     13.918      8.498         12.182         0.00          0.00   \n",
      "9      ...                  %      4.120      8.821          1.490         0.00          0.00   \n",
      "10     ...                  %     21.434      8.854         18.795         0.00          0.00   \n",
      "11     ...                  %     31.148      9.640         28.503         0.00          0.00   \n",
      "12     ...                  %     45.752      9.639         43.157         0.00          0.00   \n",
      "13     ...                  %     31.056      9.620         28.484         0.00          0.00   \n",
      "14     ...                  %     96.580     19.197         69.776         0.00          0.00   \n",
      "15     ...                  %    107.362     37.939         78.935       631.47      90439.09   \n",
      "16     ...                  %     99.187     60.757         70.627      1166.46     165720.39   \n",
      "17     ...                  %     94.601     63.047         66.049      1549.81     221707.31   \n",
      "18     ...                  %     89.988     64.547         61.405      1248.70     178063.52   \n",
      "19     ...                  %     84.777     65.724         56.148      1345.78     192602.19   \n",
      "20     ...                  %     80.837     66.934         52.202      1349.56     194496.27   \n",
      "21     ...                  %     77.338     67.848         48.708      1345.61     192899.71   \n",
      "22     ...                  %     75.949     65.707         47.376      1279.46     184900.31   \n",
      "23     ...                  %     76.118     62.796         47.611      1225.62     177107.86   \n",
      "24     ...                  %     74.690     62.377         46.196      1212.90     174608.88   \n",
      "25     ...                  %     74.729     60.747         46.150      1074.04     154870.67   \n",
      "26     ...                  %     74.160     60.628         45.733      1044.17     147929.93   \n",
      "27     ...                  %     73.908     58.279         45.491       970.20     139378.45   \n",
      "28     ...                  %     72.994     54.905         44.549       946.12     136980.49   \n",
      "29     ...                  %     70.952     58.883         42.450      1002.83     144950.15   \n",
      "..     ...                ...        ...        ...            ...          ...           ...   \n",
      "716    ...                  %     33.587     74.748         17.495       243.92      39159.61   \n",
      "717    ...                  %     34.382     68.641         18.073       246.84      40029.36   \n",
      "718    ...                  %     34.180     72.985         17.986       248.04      40439.71   \n",
      "719    ...                  %     33.238     68.793         17.028       265.31      43614.43   \n",
      "720    ...                  %     31.862     67.324         15.658       253.28      42107.87   \n",
      "721    ...                  %     30.498     73.935         14.288       271.68      44972.96   \n",
      "722    ...                  %     29.480     76.353         13.280       275.74      45175.90   \n",
      "723    ...                  %     28.396     74.630         12.212       293.43      48066.95   \n",
      "724    ...                  %     27.590     74.726         11.860       287.42      46865.43   \n",
      "725    ...                  %     27.057     67.334         11.375       299.49      48700.43   \n",
      "726    ...                  %     26.383     69.559         10.681       284.72      46880.14   \n",
      "727    ...                  %     25.681     72.502          9.910       280.17      45842.82   \n",
      "728    ...                  %     25.115     71.750          9.197       281.93      45800.83   \n",
      "729    ...                  %     25.341     72.218          9.101       317.38      51990.65   \n",
      "730    ...                  %     25.608     66.568          9.830       208.00      34998.30   \n",
      "731    ...                  %     37.062     19.720         14.370         0.00          0.00   \n",
      "732    ...                  %     10.820      7.476          4.903         0.00          0.00   \n",
      "733    ...                  %      2.820      2.078          3.856         0.00          0.00   \n",
      "734    ...                  %      8.666      6.893          0.068         0.00          0.00   \n",
      "735    ...                  %      7.460      9.140          0.216         0.00          0.00   \n",
      "736    ...                  %      6.445     10.011          0.784         0.00          0.00   \n",
      "737    ...                  %      7.107     10.237          0.496         0.00          0.00   \n",
      "738    ...                  %      7.772     11.299          0.249         0.00          0.00   \n",
      "739    ...                  %      8.263     10.540          0.077         0.00          0.00   \n",
      "740    ...                  %      8.675     10.079          0.068         0.00          0.00   \n",
      "741    ...                  %      6.498      9.802          2.645         0.00          0.00   \n",
      "742    ...                  %     13.714     11.742          4.120         0.00          0.00   \n",
      "743    ...                  %      8.576     10.119          1.866         0.00          0.00   \n",
      "744    ...                  %     18.280      9.872          7.414         0.00          0.00   \n",
      "745    ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "\n",
      "     BORE_WAT_VOL  BORE_WI_VOL   FLOW_KIND  WELL_TYPE  \n",
      "0            0.00          NaN  production         WI  \n",
      "1            0.00          NaN  production         OP  \n",
      "2            0.00          NaN  production         OP  \n",
      "3            0.00          NaN  production         OP  \n",
      "4            0.00          NaN  production         OP  \n",
      "5            0.00          NaN  production         OP  \n",
      "6            0.00          NaN  production         OP  \n",
      "7            0.00          NaN  production         OP  \n",
      "8            0.00          NaN  production         OP  \n",
      "9            0.00          NaN  production         OP  \n",
      "10           0.00          NaN  production         OP  \n",
      "11           0.00          NaN  production         OP  \n",
      "12           0.00          NaN  production         OP  \n",
      "13           0.00          NaN  production         OP  \n",
      "14           0.00          NaN  production         OP  \n",
      "15           0.00          NaN  production         OP  \n",
      "16           0.00          NaN  production         OP  \n",
      "17           0.00          NaN  production         OP  \n",
      "18           0.00          NaN  production         OP  \n",
      "19           0.00          NaN  production         OP  \n",
      "20           0.00          NaN  production         OP  \n",
      "21           0.00          NaN  production         OP  \n",
      "22           0.00          NaN  production         OP  \n",
      "23           0.00          NaN  production         OP  \n",
      "24           0.00          NaN  production         OP  \n",
      "25           0.00          NaN  production         OP  \n",
      "26           0.00          NaN  production         OP  \n",
      "27           0.00          NaN  production         OP  \n",
      "28           0.00          NaN  production         OP  \n",
      "29           0.00          NaN  production         OP  \n",
      "..            ...          ...         ...        ...  \n",
      "716        976.46          NaN  production         OP  \n",
      "717        983.16          NaN  production         OP  \n",
      "718        935.67          NaN  production         OP  \n",
      "719        913.22          NaN  production         OP  \n",
      "720        871.58          NaN  production         OP  \n",
      "721        881.85          NaN  production         OP  \n",
      "722        894.32          NaN  production         OP  \n",
      "723        832.66          NaN  production         OP  \n",
      "724        821.07          NaN  production         OP  \n",
      "725        794.20          NaN  production         OP  \n",
      "726        766.23          NaN  production         OP  \n",
      "727        748.25          NaN  production         OP  \n",
      "728        797.30          NaN  production         OP  \n",
      "729        792.76          NaN  production         OP  \n",
      "730        542.39          NaN  production         OP  \n",
      "731          0.00          NaN  production         OP  \n",
      "732          0.00          NaN  production         OP  \n",
      "733          0.00          NaN  production         OP  \n",
      "734          0.00          NaN  production         OP  \n",
      "735          0.00          NaN  production         OP  \n",
      "736          0.00          NaN  production         OP  \n",
      "737          0.00          NaN  production         OP  \n",
      "738          0.00          NaN  production         OP  \n",
      "739          0.00          NaN  production         OP  \n",
      "740          0.00          NaN  production         OP  \n",
      "741          0.00          NaN  production         OP  \n",
      "742          0.00          NaN  production         OP  \n",
      "743          0.00          NaN  production         OP  \n",
      "744          0.00          NaN  production         OP  \n",
      "745          0.00          NaN  production         OP  \n",
      "\n",
      "[746 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       DATEPRD WELL_BORE_CODE  NPD_WELL_BORE_CODE NPD_WELL_BORE_NAME  NPD_FIELD_CODE  \\\n",
      "0   2014-04-07  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "1   2014-04-08  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "2   2014-04-09  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "3   2014-04-10  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "4   2014-04-11  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "5   2014-04-12  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "6   2014-04-13  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "7   2014-04-14  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "8   2014-04-15  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "9   2014-04-16  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "10  2014-04-17  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "11  2014-04-18  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "12  2014-04-19  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "13  2014-04-20  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "14  2014-04-21  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "15  2014-04-22  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "16  2014-04-23  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "17  2014-04-24  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "18  2014-04-25  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "19  2014-04-26  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "20  2014-04-27  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "21  2014-04-28  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "22  2014-04-29  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "23  2014-04-30  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "24  2014-05-01  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "25  2014-05-02  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "26  2014-05-03  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "27  2014-05-04  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "28  2014-05-05  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "29  2014-05-06  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "..         ...            ...                 ...                ...             ...   \n",
      "716 2016-03-23  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "717 2016-03-24  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "718 2016-03-25  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "719 2016-03-26  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "720 2016-03-27  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "721 2016-03-28  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "722 2016-03-29  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "723 2016-03-30  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "724 2016-03-31  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "725 2016-04-01  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "726 2016-04-02  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "727 2016-04-03  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "728 2016-04-04  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "729 2016-04-05  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "730 2016-04-06  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "731 2016-04-07  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "732 2016-04-08  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "733 2016-04-09  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "734 2016-04-10  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "735 2016-04-11  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "736 2016-04-12  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "737 2016-04-13  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "738 2016-04-14  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "739 2016-04-15  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "740 2016-04-16  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "741 2016-04-17  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "742 2016-04-18  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "743 2016-04-19  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "744 2016-04-20  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "745 2016-04-21  NO 15/9-F-1 C                7405         15/9-F-1 C         3420717   \n",
      "\n",
      "    NPD_FIELD_NAME  NPD_FACILITY_CODE NPD_FACILITY_NAME  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
      "0            VOLVE             369304    MÆRSK INSPIRER          0.000                  0.000   \n",
      "1            VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
      "2            VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
      "3            VOLVE             369304    MÆRSK INSPIRER          0.000                    NaN   \n",
      "4            VOLVE             369304    MÆRSK INSPIRER          0.000                310.376   \n",
      "5            VOLVE             369304    MÆRSK INSPIRER          0.000                303.501   \n",
      "6            VOLVE             369304    MÆRSK INSPIRER          0.000                303.535   \n",
      "7            VOLVE             369304    MÆRSK INSPIRER          0.000                303.782   \n",
      "8            VOLVE             369304    MÆRSK INSPIRER          0.000                303.858   \n",
      "9            VOLVE             369304    MÆRSK INSPIRER          0.000                303.792   \n",
      "10           VOLVE             369304    MÆRSK INSPIRER          0.000                304.335   \n",
      "11           VOLVE             369304    MÆRSK INSPIRER          0.000                304.849   \n",
      "12           VOLVE             369304    MÆRSK INSPIRER          0.000                305.371   \n",
      "13           VOLVE             369304    MÆRSK INSPIRER          0.000                313.871   \n",
      "14           VOLVE             369304    MÆRSK INSPIRER         11.500                301.376   \n",
      "15           VOLVE             369304    MÆRSK INSPIRER         24.000                289.421   \n",
      "16           VOLVE             369304    MÆRSK INSPIRER         24.000                270.240   \n",
      "17           VOLVE             369304    MÆRSK INSPIRER         24.000                262.843   \n",
      "18           VOLVE             369304    MÆRSK INSPIRER         24.000                255.527   \n",
      "19           VOLVE             369304    MÆRSK INSPIRER         24.000                247.199   \n",
      "20           VOLVE             369304    MÆRSK INSPIRER         24.000                240.736   \n",
      "21           VOLVE             369304    MÆRSK INSPIRER         24.000                235.021   \n",
      "22           VOLVE             369304    MÆRSK INSPIRER         24.000                232.744   \n",
      "23           VOLVE             369304    MÆRSK INSPIRER         24.000                233.298   \n",
      "24           VOLVE             369304    MÆRSK INSPIRER         24.000                231.089   \n",
      "25           VOLVE             369304    MÆRSK INSPIRER         24.000                231.419   \n",
      "26           VOLVE             369304    MÆRSK INSPIRER         24.000                230.766   \n",
      "27           VOLVE             369304    MÆRSK INSPIRER         24.000                230.596   \n",
      "28           VOLVE             369304    MÆRSK INSPIRER         24.000                229.424   \n",
      "29           VOLVE             369304    MÆRSK INSPIRER         24.000                225.902   \n",
      "..             ...                ...               ...            ...                    ...   \n",
      "716          VOLVE             369304    MÆRSK INSPIRER         24.000                255.506   \n",
      "717          VOLVE             369304    MÆRSK INSPIRER         24.000                250.393   \n",
      "718          VOLVE             369304    MÆRSK INSPIRER         24.000                246.173   \n",
      "719          VOLVE             369304    MÆRSK INSPIRER         24.000                241.959   \n",
      "720          VOLVE             369304    MÆRSK INSPIRER         23.000                238.132   \n",
      "721          VOLVE             369304    MÆRSK INSPIRER         24.000                235.061   \n",
      "722          VOLVE             369304    MÆRSK INSPIRER         24.000                231.524   \n",
      "723          VOLVE             369304    MÆRSK INSPIRER         24.000                228.853   \n",
      "724          VOLVE             369304    MÆRSK INSPIRER         24.000                226.555   \n",
      "725          VOLVE             369304    MÆRSK INSPIRER         24.000                224.568   \n",
      "726          VOLVE             369304    MÆRSK INSPIRER         24.000                223.012   \n",
      "727          VOLVE             369304    MÆRSK INSPIRER         24.000                221.813   \n",
      "728          VOLVE             369304    MÆRSK INSPIRER         24.000                220.780   \n",
      "729          VOLVE             369304    MÆRSK INSPIRER         24.000                218.752   \n",
      "730          VOLVE             369304    MÆRSK INSPIRER         20.825                218.438   \n",
      "731          VOLVE             369304    MÆRSK INSPIRER          0.000                228.199   \n",
      "732          VOLVE             369304    MÆRSK INSPIRER          0.000                234.980   \n",
      "733          VOLVE             369304    MÆRSK INSPIRER          0.000                239.915   \n",
      "734          VOLVE             369304    MÆRSK INSPIRER          0.000                244.889   \n",
      "735          VOLVE             369304    MÆRSK INSPIRER          0.000                249.408   \n",
      "736          VOLVE             369304    MÆRSK INSPIRER          0.000                253.410   \n",
      "737          VOLVE             369304    MÆRSK INSPIRER          0.000                256.990   \n",
      "738          VOLVE             369304    MÆRSK INSPIRER          0.000                260.284   \n",
      "739          VOLVE             369304    MÆRSK INSPIRER          0.000                263.327   \n",
      "740          VOLVE             369304    MÆRSK INSPIRER          0.000                266.148   \n",
      "741          VOLVE             369304    MÆRSK INSPIRER          0.000                270.268   \n",
      "742          VOLVE             369304    MÆRSK INSPIRER          0.000                276.869   \n",
      "743          VOLVE             369304    MÆRSK INSPIRER          0.000                282.105   \n",
      "744          VOLVE             369304    MÆRSK INSPIRER          0.000                285.096   \n",
      "745          VOLVE             369304    MÆRSK INSPIRER          0.000                  0.000   \n",
      "\n",
      "       ...      AVG_CHOKE_UOM  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE BORE_OIL_VOL  BORE_GAS_VOL  \\\n",
      "0      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "1      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "2      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "3      ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "4      ...                  %     33.098     10.480         33.072         0.00          0.00   \n",
      "5      ...                  %     22.053      8.704         22.053         0.00          0.00   \n",
      "6      ...                  %     27.503      9.423         16.163         0.00          0.00   \n",
      "7      ...                  %     20.996      8.131         20.737         0.00          0.00   \n",
      "8      ...                  %     13.918      8.498         12.182         0.00          0.00   \n",
      "9      ...                  %      4.120      8.821          1.490         0.00          0.00   \n",
      "10     ...                  %     21.434      8.854         18.795         0.00          0.00   \n",
      "11     ...                  %     31.148      9.640         28.503         0.00          0.00   \n",
      "12     ...                  %     45.752      9.639         43.157         0.00          0.00   \n",
      "13     ...                  %     31.056      9.620         28.484         0.00          0.00   \n",
      "14     ...                  %     96.580     19.197         69.776         0.00          0.00   \n",
      "15     ...                  %    107.362     37.939         78.935       631.47      90439.09   \n",
      "16     ...                  %     99.187     60.757         70.627      1166.46     165720.39   \n",
      "17     ...                  %     94.601     63.047         66.049      1549.81     221707.31   \n",
      "18     ...                  %     89.988     64.547         61.405      1248.70     178063.52   \n",
      "19     ...                  %     84.777     65.724         56.148      1345.78     192602.19   \n",
      "20     ...                  %     80.837     66.934         52.202      1349.56     194496.27   \n",
      "21     ...                  %     77.338     67.848         48.708      1345.61     192899.71   \n",
      "22     ...                  %     75.949     65.707         47.376      1279.46     184900.31   \n",
      "23     ...                  %     76.118     62.796         47.611      1225.62     177107.86   \n",
      "24     ...                  %     74.690     62.377         46.196      1212.90     174608.88   \n",
      "25     ...                  %     74.729     60.747         46.150      1074.04     154870.67   \n",
      "26     ...                  %     74.160     60.628         45.733      1044.17     147929.93   \n",
      "27     ...                  %     73.908     58.279         45.491       970.20     139378.45   \n",
      "28     ...                  %     72.994     54.905         44.549       946.12     136980.49   \n",
      "29     ...                  %     70.952     58.883         42.450      1002.83     144950.15   \n",
      "..     ...                ...        ...        ...            ...          ...           ...   \n",
      "716    ...                  %     33.587     74.748         17.495       243.92      39159.61   \n",
      "717    ...                  %     34.382     68.641         18.073       246.84      40029.36   \n",
      "718    ...                  %     34.180     72.985         17.986       248.04      40439.71   \n",
      "719    ...                  %     33.238     68.793         17.028       265.31      43614.43   \n",
      "720    ...                  %     31.862     67.324         15.658       253.28      42107.87   \n",
      "721    ...                  %     30.498     73.935         14.288       271.68      44972.96   \n",
      "722    ...                  %     29.480     76.353         13.280       275.74      45175.90   \n",
      "723    ...                  %     28.396     74.630         12.212       293.43      48066.95   \n",
      "724    ...                  %     27.590     74.726         11.860       287.42      46865.43   \n",
      "725    ...                  %     27.057     67.334         11.375       299.49      48700.43   \n",
      "726    ...                  %     26.383     69.559         10.681       284.72      46880.14   \n",
      "727    ...                  %     25.681     72.502          9.910       280.17      45842.82   \n",
      "728    ...                  %     25.115     71.750          9.197       281.93      45800.83   \n",
      "729    ...                  %     25.341     72.218          9.101       317.38      51990.65   \n",
      "730    ...                  %     25.608     66.568          9.830       208.00      34998.30   \n",
      "731    ...                  %     37.062     19.720         14.370         0.00          0.00   \n",
      "732    ...                  %     10.820      7.476          4.903         0.00          0.00   \n",
      "733    ...                  %      2.820      2.078          3.856         0.00          0.00   \n",
      "734    ...                  %      8.666      6.893          0.068         0.00          0.00   \n",
      "735    ...                  %      7.460      9.140          0.216         0.00          0.00   \n",
      "736    ...                  %      6.445     10.011          0.784         0.00          0.00   \n",
      "737    ...                  %      7.107     10.237          0.496         0.00          0.00   \n",
      "738    ...                  %      7.772     11.299          0.249         0.00          0.00   \n",
      "739    ...                  %      8.263     10.540          0.077         0.00          0.00   \n",
      "740    ...                  %      8.675     10.079          0.068         0.00          0.00   \n",
      "741    ...                  %      6.498      9.802          2.645         0.00          0.00   \n",
      "742    ...                  %     13.714     11.742          4.120         0.00          0.00   \n",
      "743    ...                  %      8.576     10.119          1.866         0.00          0.00   \n",
      "744    ...                  %     18.280      9.872          7.414         0.00          0.00   \n",
      "745    ...                  %      0.000      0.000          0.000         0.00          0.00   \n",
      "\n",
      "     BORE_WAT_VOL  BORE_WI_VOL   FLOW_KIND  WELL_TYPE  \n",
      "0            0.00          NaN  production         WI  \n",
      "1            0.00          NaN  production         OP  \n",
      "2            0.00          NaN  production         OP  \n",
      "3            0.00          NaN  production         OP  \n",
      "4            0.00          NaN  production         OP  \n",
      "5            0.00          NaN  production         OP  \n",
      "6            0.00          NaN  production         OP  \n",
      "7            0.00          NaN  production         OP  \n",
      "8            0.00          NaN  production         OP  \n",
      "9            0.00          NaN  production         OP  \n",
      "10           0.00          NaN  production         OP  \n",
      "11           0.00          NaN  production         OP  \n",
      "12           0.00          NaN  production         OP  \n",
      "13           0.00          NaN  production         OP  \n",
      "14           0.00          NaN  production         OP  \n",
      "15           0.00          NaN  production         OP  \n",
      "16           0.00          NaN  production         OP  \n",
      "17           0.00          NaN  production         OP  \n",
      "18           0.00          NaN  production         OP  \n",
      "19           0.00          NaN  production         OP  \n",
      "20           0.00          NaN  production         OP  \n",
      "21           0.00          NaN  production         OP  \n",
      "22           0.00          NaN  production         OP  \n",
      "23           0.00          NaN  production         OP  \n",
      "24           0.00          NaN  production         OP  \n",
      "25           0.00          NaN  production         OP  \n",
      "26           0.00          NaN  production         OP  \n",
      "27           0.00          NaN  production         OP  \n",
      "28           0.00          NaN  production         OP  \n",
      "29           0.00          NaN  production         OP  \n",
      "..            ...          ...         ...        ...  \n",
      "716        976.46          NaN  production         OP  \n",
      "717        983.16          NaN  production         OP  \n",
      "718        935.67          NaN  production         OP  \n",
      "719        913.22          NaN  production         OP  \n",
      "720        871.58          NaN  production         OP  \n",
      "721        881.85          NaN  production         OP  \n",
      "722        894.32          NaN  production         OP  \n",
      "723        832.66          NaN  production         OP  \n",
      "724        821.07          NaN  production         OP  \n",
      "725        794.20          NaN  production         OP  \n",
      "726        766.23          NaN  production         OP  \n",
      "727        748.25          NaN  production         OP  \n",
      "728        797.30          NaN  production         OP  \n",
      "729        792.76          NaN  production         OP  \n",
      "730        542.39          NaN  production         OP  \n",
      "731          0.00          NaN  production         OP  \n",
      "732          0.00          NaN  production         OP  \n",
      "733          0.00          NaN  production         OP  \n",
      "734          0.00          NaN  production         OP  \n",
      "735          0.00          NaN  production         OP  \n",
      "736          0.00          NaN  production         OP  \n",
      "737          0.00          NaN  production         OP  \n",
      "738          0.00          NaN  production         OP  \n",
      "739          0.00          NaN  production         OP  \n",
      "740          0.00          NaN  production         OP  \n",
      "741          0.00          NaN  production         OP  \n",
      "742          0.00          NaN  production         OP  \n",
      "743          0.00          NaN  production         OP  \n",
      "744          0.00          NaN  production         OP  \n",
      "745          0.00          NaN  production         OP  \n",
      "\n",
      "[746 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "X1.dropna()\n",
    "print(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Add production volumes of all products\n",
    "TotVol = X1['BORE_OIL_VOL']+X1['BORE_GAS_VOL']+X1['BORE_WAT_VOL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.00\n",
       "1         0.00\n",
       "2         0.00\n",
       "3         0.00\n",
       "4         0.00\n",
       "5         0.00\n",
       "6         0.00\n",
       "7         0.00\n",
       "8         0.00\n",
       "9         0.00\n",
       "10        0.00\n",
       "11        0.00\n",
       "12        0.00\n",
       "13        0.00\n",
       "14        0.00\n",
       "15      631.47\n",
       "16     1166.46\n",
       "17     1549.81\n",
       "18     1248.70\n",
       "19     1345.78\n",
       "20     1349.56\n",
       "21     1345.61\n",
       "22     1279.46\n",
       "23     1225.62\n",
       "24     1212.90\n",
       "25     1074.04\n",
       "26     1044.17\n",
       "27      970.20\n",
       "28      946.12\n",
       "29     1002.83\n",
       "        ...   \n",
       "716     243.92\n",
       "717     246.84\n",
       "718     248.04\n",
       "719     265.31\n",
       "720     253.28\n",
       "721     271.68\n",
       "722     275.74\n",
       "723     293.43\n",
       "724     287.42\n",
       "725     299.49\n",
       "726     284.72\n",
       "727     280.17\n",
       "728     281.93\n",
       "729     317.38\n",
       "730     208.00\n",
       "731       0.00\n",
       "732       0.00\n",
       "733       0.00\n",
       "734       0.00\n",
       "735       0.00\n",
       "736       0.00\n",
       "737       0.00\n",
       "738       0.00\n",
       "739       0.00\n",
       "740       0.00\n",
       "741       0.00\n",
       "742       0.00\n",
       "743       0.00\n",
       "744       0.00\n",
       "745       0.00\n",
       "Name: BORE_OIL_VOL, Length: 746, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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